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0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" 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</div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button 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seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header Overlay-header--divided"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="custom-scopes-dialog-title"> Saved searches </h1> <h2 id="custom-scopes-dialog-description" class="Overlay-description">Use saved searches to filter your results more quickly</h2> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="custom-scopes-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="custom-scopes-dialog-title"> <div data-view-component="true" class="Overlay-body"> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <div hidden class="create-custom-scope-form" data-target="custom-scopes.createCustomScopeForm"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" 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16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eImplementation of papers in 100 lines of code.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eImplemented papers\u003c/h2\u003e\u003ca id=\"user-content-implemented-papers\" class=\"anchor\" aria-label=\"Permalink: Implemented papers\" href=\"#implemented-papers\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Maxout Networks]\u003c/h5\u003e\u003ca id=\"user-content-maxout-networks\" class=\"anchor\" aria-label=\"Permalink: [Maxout Networks]\" href=\"#maxout-networks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMaxout Networks \u003ca href=\"https://arxiv.org/abs/1302.4389\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eIan J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua Bengio\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2013-02-18\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Network In Network]\u003c/h5\u003e\u003ca id=\"user-content-network-in-network\" class=\"anchor\" aria-label=\"Permalink: [Network In Network]\" href=\"#network-in-network\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eNetwork In Network \u003ca href=\"https://arxiv.org/abs/1312.4400\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eMin Lin, Qiang Chen, Shuicheng Yan\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2013-12-13\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Playing Atari with Deep Reinforcement Learning]\u003c/h5\u003e\u003ca id=\"user-content-playing-atari-with-deep-reinforcement-learning\" class=\"anchor\" aria-label=\"Permalink: [Playing Atari with Deep Reinforcement Learning]\" href=\"#playing-atari-with-deep-reinforcement-learning\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePlaying Atari with Deep Reinforcement Learning \u003ca href=\"https://arxiv.org/abs/1312.5602\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eVolodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2013-12-19\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Auto-Encoding Variational Bayes]\u003c/h5\u003e\u003ca id=\"user-content-auto-encoding-variational-bayes\" class=\"anchor\" aria-label=\"Permalink: [Auto-Encoding Variational Bayes]\" href=\"#auto-encoding-variational-bayes\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAuto-Encoding Variational Bayes \u003ca href=\"https://arxiv.org/abs/1312.6114\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eDiederik P Kingma, Max Welling\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2013-12-20\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Generative Adversarial Networks]\u003c/h5\u003e\u003ca id=\"user-content-generative-adversarial-networks\" class=\"anchor\" aria-label=\"Permalink: [Generative Adversarial Networks]\" href=\"#generative-adversarial-networks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGenerative Adversarial Networks \u003ca href=\"https://arxiv.org/abs/1406.2661\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eIan J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2014-06-10\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Conditional Generative Adversarial Nets]\u003c/h5\u003e\u003ca id=\"user-content-conditional-generative-adversarial-nets\" class=\"anchor\" aria-label=\"Permalink: [Conditional Generative Adversarial Nets]\" href=\"#conditional-generative-adversarial-nets\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eConditional Generative Adversarial Nets \u003ca href=\"https://arxiv.org/abs/1411.1784\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eMehdi Mirza, Simon Osindero\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2014-11-06\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Adam: A Method for Stochastic Optimization]\u003c/h5\u003e\u003ca id=\"user-content-adam-a-method-for-stochastic-optimization\" class=\"anchor\" aria-label=\"Permalink: [Adam: A Method for Stochastic Optimization]\" href=\"#adam-a-method-for-stochastic-optimization\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAdam: A Method for Stochastic Optimization \u003ca href=\"https://arxiv.org/abs/1412.6980\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eDiederik P. Kingma, Jimmy Ba\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2014-12-22\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[NICE: Non-linear Independent Components Estimation]\u003c/h5\u003e\u003ca id=\"user-content-nice-non-linear-independent-components-estimation\" class=\"anchor\" aria-label=\"Permalink: [NICE: Non-linear Independent Components Estimation]\" href=\"#nice-non-linear-independent-components-estimation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eNICE: Non-linear Independent Components Estimation \u003ca href=\"https://arxiv.org/abs/1410.8516\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eLaurent Dinh, David Krueger, Yoshua Bengio\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2014-10-30\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Human-level control through deep reinforcement learning]\u003c/h5\u003e\u003ca id=\"user-content-human-level-control-through-deep-reinforcement-learning\" class=\"anchor\" aria-label=\"Permalink: [Human-level control through deep reinforcement learning]\" href=\"#human-level-control-through-deep-reinforcement-learning\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHuman-level control through deep reinforcement learning \u003ca href=\"https://www.nature.com/articles/nature14236\" rel=\"nofollow\"\u003e[nature]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eVolodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg \u0026amp; Demis Hassabis\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2015-02-25\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Deep Unsupervised Learning using Nonequilibrium Thermodynamics]\u003c/h5\u003e\u003ca id=\"user-content-deep-unsupervised-learning-using-nonequilibrium-thermodynamics\" class=\"anchor\" aria-label=\"Permalink: [Deep Unsupervised Learning using Nonequilibrium Thermodynamics]\" href=\"#deep-unsupervised-learning-using-nonequilibrium-thermodynamics\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDeep Unsupervised Learning using Nonequilibrium Thermodynamics \u003ca href=\"https://arxiv.org/abs/1503.03585\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eJascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2015-03-12\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Variational Inference with Normalizing Flows]\u003c/h5\u003e\u003ca id=\"user-content-variational-inference-with-normalizing-flows\" class=\"anchor\" aria-label=\"Permalink: [Variational Inference with Normalizing Flows]\" href=\"#variational-inference-with-normalizing-flows\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eVariational Inference with Normalizing Flows \u003ca href=\"https://arxiv.org/abs/1505.05770\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eDanilo Jimenez Rezende, Shakir Mohamed\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2015-05-21\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Deep Reinforcement Learning with Double Q-learning]\u003c/h5\u003e\u003ca id=\"user-content-deep-reinforcement-learning-with-double-q-learning\" class=\"anchor\" aria-label=\"Permalink: [Deep Reinforcement Learning with Double Q-learning]\" href=\"#deep-reinforcement-learning-with-double-q-learning\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDeep Reinforcement Learning with Double Q-learning \u003ca href=\"https://arxiv.org/abs/1509.06461\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eHado van Hasselt, Arthur Guez, David Silver\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2015-09-22\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks]\u003c/h5\u003e\u003ca id=\"user-content-unsupervised-representation-learning-with-deep-convolutional-generative-adversarial-networks\" class=\"anchor\" aria-label=\"Permalink: [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks]\" href=\"#unsupervised-representation-learning-with-deep-convolutional-generative-adversarial-networks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eConvolutional Generative Adversarial Networks \u003ca href=\"https://arxiv.org/abs/1511.06434\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eAlec Radford, Luke Metz, Soumith Chintala\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2015-11-19\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)]\u003c/h5\u003e\u003ca id=\"user-content-fast-and-accurate-deep-network-learning-by-exponential-linear-units-elus\" class=\"anchor\" aria-label=\"Permalink: [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)]\" href=\"#fast-and-accurate-deep-network-learning-by-exponential-linear-units-elus\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) \u003ca href=\"https://arxiv.org/abs/1511.07289\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eDjork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2015-11-23\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Adversarially Learned Inference]\u003c/h5\u003e\u003ca id=\"user-content-adversarially-learned-inference\" class=\"anchor\" aria-label=\"Permalink: [Adversarially Learned Inference]\" href=\"#adversarially-learned-inference\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAdversarially Learned Inference \u003ca href=\"https://arxiv.org/abs/1606.00704\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eVincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, Aaron Courville\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2016-06-02\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Improved Techniques for Training GANs]\u003c/h5\u003e\u003ca id=\"user-content-improved-techniques-for-training-gans\" class=\"anchor\" aria-label=\"Permalink: [Improved Techniques for Training GANs]\" href=\"#improved-techniques-for-training-gans\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eImproved Techniques for Training GANs \u003ca href=\"https://arxiv.org/abs/1606.03498\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eTim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2016-06-10\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Gaussian Error Linear Units (GELUs)]\u003c/h5\u003e\u003ca id=\"user-content-gaussian-error-linear-units-gelus\" class=\"anchor\" aria-label=\"Permalink: [Gaussian Error Linear Units (GELUs)]\" href=\"#gaussian-error-linear-units-gelus\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGaussian Error Linear Units (GELUs) \u003ca href=\"https://arxiv.org/abs/1606.08415\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eDan Hendrycks, Kevin Gimpel\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2016-06-27\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Least Squares Generative Adversarial Networks]\u003c/h5\u003e\u003ca id=\"user-content-least-squares-generative-adversarial-networks\" class=\"anchor\" aria-label=\"Permalink: [Least Squares Generative Adversarial Networks]\" href=\"#least-squares-generative-adversarial-networks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLeast Squares Generative Adversarial Networks \u003ca href=\"https://arxiv.org/abs/1611.04076\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eXudong Mao, Qing Li, Haoran Xie, Raymond Y.K. Lau, Zhen Wang, Stephen Paul Smolley\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2016-11-13\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Image-to-Image Translation with Conditional Adversarial Networks]\u003c/h5\u003e\u003ca id=\"user-content-image-to-image-translation-with-conditional-adversarial-networks\" class=\"anchor\" aria-label=\"Permalink: [Image-to-Image Translation with Conditional Adversarial Networks]\" href=\"#image-to-image-translation-with-conditional-adversarial-networks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eImage-to-Image Translation with Conditional Adversarial Networks \u003ca href=\"https://arxiv.org/abs/1611.07004\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003ePhillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2016-11-21\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Wasserstein GAN]\u003c/h5\u003e\u003ca id=\"user-content-wasserstein-gan\" class=\"anchor\" aria-label=\"Permalink: [Wasserstein GAN]\" href=\"#wasserstein-gan\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eWasserstein GAN \u003ca href=\"https://arxiv.org/abs/1701.07875\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eMartin Arjovsky, Soumith Chintala, Léon Bottou\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2017-01-26\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks]\u003c/h5\u003e\u003ca id=\"user-content-model-agnostic-meta-learning-for-fast-adaptation-of-deep-networks\" class=\"anchor\" aria-label=\"Permalink: [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks]\" href=\"#model-agnostic-meta-learning-for-fast-adaptation-of-deep-networks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eModel-Agnostic Meta-Learning for Fast Adaptation of Deep Networks \u003ca href=\"https://arxiv.org/abs/1703.03400\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eChelsea Finn, Pieter Abbeel, Sergey Levine\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2017-03-09\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks]\u003c/h5\u003e\u003ca id=\"user-content-unpaired-image-to-image-translation-using-cycle-consistent-adversarial-networks\" class=\"anchor\" aria-label=\"Permalink: [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks]\" href=\"#unpaired-image-to-image-translation-using-cycle-consistent-adversarial-networks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eUnpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks \u003ca href=\"https://arxiv.org/abs/1703.10593\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eJun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2017-03-30\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Improved Training of Wasserstein GANs]\u003c/h5\u003e\u003ca id=\"user-content-improved-training-of-wasserstein-gans\" class=\"anchor\" aria-label=\"Permalink: [Improved Training of Wasserstein GANs]\" href=\"#improved-training-of-wasserstein-gans\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eImproved Training of Wasserstein GANs \u003ca href=\"https://arxiv.org/abs/1704.00028\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eIshaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron Courville\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2017-03-31\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Adversarial Feature Learning]\u003c/h5\u003e\u003ca id=\"user-content-adversarial-feature-learning\" class=\"anchor\" aria-label=\"Permalink: [Adversarial Feature Learning]\" href=\"#adversarial-feature-learning\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAdversarial Feature Learning \u003ca href=\"https://arxiv.org/abs/1605.09782\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eJeff Donahue, Philipp Krähenbühl, Trevor Darrell\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2017-04-03\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Self-Normalizing Neural Networks]\u003c/h5\u003e\u003ca id=\"user-content-self-normalizing-neural-networks\" class=\"anchor\" aria-label=\"Permalink: [Self-Normalizing Neural Networks]\" href=\"#self-normalizing-neural-networks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSelf-Normalizing Neural Networks \u003ca href=\"https://arxiv.org/abs/1706.02515\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eGünter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2017-06-08\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Proximal Policy Optimization Algorithms]\u003c/h5\u003e\u003ca id=\"user-content-proximal-policy-optimization-algorithms\" class=\"anchor\" aria-label=\"Permalink: [Proximal Policy Optimization Algorithms]\" href=\"#proximal-policy-optimization-algorithms\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eProximal Policy Optimization Algorithms \u003ca href=\"https://arxiv.org/abs/1707.06347\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eJohn Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2017-08-28\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Deep Image Prior]\u003c/h5\u003e\u003ca id=\"user-content-deep-image-prior\" class=\"anchor\" aria-label=\"Permalink: [Deep Image Prior]\" href=\"#deep-image-prior\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDeep Image Prior \u003ca href=\"https://arxiv.org/abs/1711.10925\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eDmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2017-11-29\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[On First-Order Meta-Learning Algorithms]\u003c/h5\u003e\u003ca id=\"user-content-on-first-order-meta-learning-algorithms\" class=\"anchor\" aria-label=\"Permalink: [On First-Order Meta-Learning Algorithms]\" href=\"#on-first-order-meta-learning-algorithms\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eOn First-Order Meta-Learning Algorithms \u003ca href=\"https://arxiv.org/abs/1803.02999\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eAlex Nichol, Joshua Achiam, John Schulman\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2018-03-08\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Sequential Neural Likelihood]\u003c/h5\u003e\u003ca id=\"user-content-sequential-neural-likelihood\" class=\"anchor\" aria-label=\"Permalink: [Sequential Neural Likelihood]\" href=\"#sequential-neural-likelihood\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows \u003ca href=\"https://arxiv.org/abs/1805.07226\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eGeorge Papamakarios, David C. Sterratt, Iain Murray\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2018-05-18\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[On the Variance of the Adaptive Learning Rate and Beyond]\u003c/h5\u003e\u003ca id=\"user-content-on-the-variance-of-the-adaptive-learning-rate-and-beyond\" class=\"anchor\" aria-label=\"Permalink: [On the Variance of the Adaptive Learning Rate and Beyond]\" href=\"#on-the-variance-of-the-adaptive-learning-rate-and-beyond\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eOn the Variance of the Adaptive Learning Rate and Beyond \u003ca href=\"https://arxiv.org/abs/1908.03265\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eLiyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, Jiawei Han\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2019-08-08\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Optimizing Millions of Hyperparameters by Implicit Differentiation]\u003c/h5\u003e\u003ca id=\"user-content-optimizing-millions-of-hyperparameters-by-implicit-differentiation\" class=\"anchor\" aria-label=\"Permalink: [Optimizing Millions of Hyperparameters by Implicit Differentiation]\" href=\"#optimizing-millions-of-hyperparameters-by-implicit-differentiation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eOptimizing Millions of Hyperparameters by Implicit Differentiation \u003ca href=\"https://proceedings.mlr.press/v108/lorraine20a\" rel=\"nofollow\"\u003e[PMLR]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eJonathan Lorraine, Paul Vicol, David Duvenaud\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2019-10-06\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Implicit Neural Representations with Periodic Activation Functions]\u003c/h5\u003e\u003ca id=\"user-content-implicit-neural-representations-with-periodic-activation-functions\" class=\"anchor\" aria-label=\"Permalink: [Implicit Neural Representations with Periodic Activation Functions]\" href=\"#implicit-neural-representations-with-periodic-activation-functions\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eImplicit Neural Representations with Periodic Activation Functions \u003ca href=\"https://arxiv.org/abs/2006.09661\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eVincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, Gordon Wetzstein\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2020-06-17\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains]\u003c/h5\u003e\u003ca id=\"user-content-fourier-features-let-networks-learn-high-frequency-functions-in-low-dimensional-domains\" class=\"anchor\" aria-label=\"Permalink: [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains]\" href=\"#fourier-features-let-networks-learn-high-frequency-functions-in-low-dimensional-domains\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains \u003ca href=\"https://arxiv.org/abs/2006.10739\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eMatthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, Ren Ng\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2020-06-18\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Denoising Diffusion Probabilistic Models]\u003c/h5\u003e\u003ca id=\"user-content-denoising-diffusion-probabilistic-models\" class=\"anchor\" aria-label=\"Permalink: [Denoising Diffusion Probabilistic Models]\" href=\"#denoising-diffusion-probabilistic-models\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDenoising Diffusion Probabilistic Models \u003ca href=\"https://arxiv.org/abs/2006.11239\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eJonathan Ho, Ajay Jain, Pieter Abbeel\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2020-06-19\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Likelihood-free MCMC with Amortized Approximate Ratio Estimators]\u003c/h5\u003e\u003ca id=\"user-content-likelihood-free-mcmc-with-amortized-approximate-ratio-estimators\" class=\"anchor\" aria-label=\"Permalink: [Likelihood-free MCMC with Amortized Approximate Ratio Estimators]\" href=\"#likelihood-free-mcmc-with-amortized-approximate-ratio-estimators\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLikelihood-free MCMC with Amortized Approximate Ratio Estimators \u003ca href=\"http://proceedings.mlr.press/v108/lorraine20a\" rel=\"nofollow\"\u003e[PMLR]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eJoeri Hermans, Volodimir Begy, Gilles Louppe\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2020-06-26\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis]\u003c/h5\u003e\u003ca id=\"user-content-nerf-representing-scenes-as-neural-radiance-fields-for-view-synthesis\" class=\"anchor\" aria-label=\"Permalink: [NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis]\" href=\"#nerf-representing-scenes-as-neural-radiance-fields-for-view-synthesis\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eNeRF: Representing Scenes as Neural Radiance Fields for View Synthesis \u003ca href=\"https://arxiv.org/abs/2003.08934\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eBen Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2020-08-03\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Multiplicative Filter Networks]\u003c/h5\u003e\u003ca id=\"user-content-multiplicative-filter-networks\" class=\"anchor\" aria-label=\"Permalink: [Multiplicative Filter Networks]\" href=\"#multiplicative-filter-networks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMultiplicative Filter Networks \u003ca href=\"https://openreview.net/forum?id=OmtmcPkkhT\" rel=\"nofollow\"\u003e[OpenReview]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eRizal Fathony, Anit Kumar Sahu, Devin Willmott, J Zico Kolter\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2020-09-28\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Learned Initializations for Optimizing Coordinate-Based Neural Representations]\u003c/h5\u003e\u003ca id=\"user-content-learned-initializations-for-optimizing-coordinate-based-neural-representations\" class=\"anchor\" aria-label=\"Permalink: [Learned Initializations for Optimizing Coordinate-Based Neural Representations]\" href=\"#learned-initializations-for-optimizing-coordinate-based-neural-representations\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLearned Initializations for Optimizing Coordinate-Based Neural Representations \u003ca href=\"https://arxiv.org/abs/2012.02189\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eMatthew Tancik, Ben Mildenhall, Terrance Wang, Divi Schmidt, Pratul P. Srinivasan, Jonathan T. Barron, Ren Ng\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2020-12-03\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[FastNeRF: High-Fidelity Neural Rendering at 200FPS]\u003c/h5\u003e\u003ca id=\"user-content-fastnerf-high-fidelity-neural-rendering-at-200fps\" class=\"anchor\" aria-label=\"Permalink: [FastNeRF: High-Fidelity Neural Rendering at 200FPS]\" href=\"#fastnerf-high-fidelity-neural-rendering-at-200fps\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFastNeRF: High-Fidelity Neural Rendering at 200FPS \u003ca href=\"https://arxiv.org/abs/2103.10380\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eStephan J. Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, Julien Valentin\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2021-03-18\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs]\u003c/h5\u003e\u003ca id=\"user-content-kilonerf-speeding-up-neural-radiance-fields-with-thousands-of-tiny-mlps\" class=\"anchor\" aria-label=\"Permalink: [KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs]\" href=\"#kilonerf-speeding-up-neural-radiance-fields-with-thousands-of-tiny-mlps\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eKiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs \u003ca href=\"https://arxiv.org/abs/2103.13744\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eChristian Reiser, Songyou Peng, Yiyi Liao, Andreas Geiger\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2021-03-25\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[PlenOctrees for Real-time Rendering of Neural Radiance Fields]\u003c/h5\u003e\u003ca id=\"user-content-plenoctrees-for-real-time-rendering-of-neural-radiance-fields\" class=\"anchor\" aria-label=\"Permalink: [PlenOctrees for Real-time Rendering of Neural Radiance Fields]\" href=\"#plenoctrees-for-real-time-rendering-of-neural-radiance-fields\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePlenOctrees for Real-time Rendering of Neural Radiance Fields \u003ca href=\"https://arxiv.org/abs/2103.14024\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eAlex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, Angjoo Kanazawa\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2021-03-25\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[NeRF--: Neural Radiance Fields Without Known Camera Parameters]\u003c/h5\u003e\u003ca id=\"user-content-nerf---neural-radiance-fields-without-known-camera-parameters\" class=\"anchor\" aria-label=\"Permalink: [NeRF--: Neural Radiance Fields Without Known Camera Parameters]\" href=\"#nerf---neural-radiance-fields-without-known-camera-parameters\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eNeRF--: Neural Radiance Fields Without Known Camera Parameters \u003ca href=\"https://arxiv.org/abs/2102.07064\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eZirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, Victor Adrian Prisacariu\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2021-02-14\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Gromov-Wasserstein Distances between Gaussian Distributions]\u003c/h5\u003e\u003ca id=\"user-content-gromov-wasserstein-distances-between-gaussian-distributions\" class=\"anchor\" aria-label=\"Permalink: [Gromov-Wasserstein Distances between Gaussian Distributions]\" href=\"#gromov-wasserstein-distances-between-gaussian-distributions\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGromov-Wasserstein Distances between Gaussian Distributions \u003ca href=\"https://arxiv.org/abs/2104.07970\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eAntoine Salmona, Julie Delon, Agnès Desolneux\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2021-08-16\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Plenoxels: Radiance Fields without Neural Networks]\u003c/h5\u003e\u003ca id=\"user-content-plenoxels-radiance-fields-without-neural-networks\" class=\"anchor\" aria-label=\"Permalink: [Plenoxels: Radiance Fields without Neural Networks]\" href=\"#plenoxels-radiance-fields-without-neural-networks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePlenoxels: Radiance Fields without Neural Networks \u003ca href=\"https://arxiv.org/abs/2112.05131\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eAlex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2021-12-09\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering]\u003c/h5\u003e\u003ca id=\"user-content-infonerf-ray-entropy-minimization-for-few-shot-neural-volume-rendering\" class=\"anchor\" aria-label=\"Permalink: [InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering]\" href=\"#infonerf-ray-entropy-minimization-for-few-shot-neural-volume-rendering\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eInfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering \u003ca href=\"https://arxiv.org/abs/2112.15399\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eMijeong Kim, Seonguk Seo, Bohyung Han\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2021-12-31\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Instant Neural Graphics Primitives with a Multiresolution Hash Encoding]\u003c/h5\u003e\u003ca id=\"user-content-instant-neural-graphics-primitives-with-a-multiresolution-hash-encoding\" class=\"anchor\" aria-label=\"Permalink: [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding]\" href=\"#instant-neural-graphics-primitives-with-a-multiresolution-hash-encoding\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eInstant Neural Graphics Primitives with a Multiresolution Hash Encoding \u003ca href=\"https://arxiv.org/abs/2201.05989\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eThomas Müller, Alex Evans, Christoph Schied, Alexander Keller\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2022-01-16\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow]\u003c/h5\u003e\u003ca id=\"user-content-flow-straight-and-fast-learning-to-generate-and-transfer-data-with-rectified-flow\" class=\"anchor\" aria-label=\"Permalink: [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow]\" href=\"#flow-straight-and-fast-learning-to-generate-and-transfer-data-with-rectified-flow\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFlow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow \u003ca href=\"https://arxiv.org/abs/2209.03003\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eXingchao Liu, Chengyue Gong, Qiang Liu\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2022-09-07\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[K-Planes: Explicit Radiance Fields in Space, Time, and Appearance]\u003c/h5\u003e\u003ca id=\"user-content-k-planes-explicit-radiance-fields-in-space-time-and-appearance\" class=\"anchor\" aria-label=\"Permalink: [K-Planes: Explicit Radiance Fields in Space, Time, and Appearance]\" href=\"#k-planes-explicit-radiance-fields-in-space-time-and-appearance\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eK-Planes: Explicit Radiance Fields in Space, Time, and Appearance \u003ca href=\"https://arxiv.org/abs/2301.10241\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eSara Fridovich-Keil, Giacomo Meanti, Frederik Warburg, Benjamin Recht, Angjoo Kanazawa\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2023-01-24\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization]\u003c/h5\u003e\u003ca id=\"user-content-freenerf-improving-few-shot-neural-rendering-with-free-frequency-regularization\" class=\"anchor\" aria-label=\"Permalink: [FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization]\" href=\"#freenerf-improving-few-shot-neural-rendering-with-free-frequency-regularization\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization \u003ca href=\"https://arxiv.org/abs/2303.07418\" rel=\"nofollow\"\u003e[arXiv]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eJiawei Yang, Marco Pavone, Yue Wang\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e2023-03-13\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Papers in 100 Lines of Code","anchor":"papers-in-100-lines-of-code","htmlText":"Papers in 100 Lines of Code"},{"level":2,"text":"Implemented papers","anchor":"implemented-papers","htmlText":"Implemented papers"},{"level":5,"text":"[Maxout Networks]","anchor":"maxout-networks","htmlText":"[Maxout Networks]"},{"level":5,"text":"[Network In Network]","anchor":"network-in-network","htmlText":"[Network In Network]"},{"level":5,"text":"[Playing Atari with Deep Reinforcement Learning]","anchor":"playing-atari-with-deep-reinforcement-learning","htmlText":"[Playing Atari with Deep Reinforcement Learning]"},{"level":5,"text":"[Auto-Encoding Variational Bayes]","anchor":"auto-encoding-variational-bayes","htmlText":"[Auto-Encoding Variational Bayes]"},{"level":5,"text":"[Generative Adversarial Networks]","anchor":"generative-adversarial-networks","htmlText":"[Generative Adversarial Networks]"},{"level":5,"text":"[Conditional Generative Adversarial Nets]","anchor":"conditional-generative-adversarial-nets","htmlText":"[Conditional Generative Adversarial Nets]"},{"level":5,"text":"[Adam: A Method for Stochastic Optimization]","anchor":"adam-a-method-for-stochastic-optimization","htmlText":"[Adam: A Method for Stochastic Optimization]"},{"level":5,"text":"[NICE: Non-linear Independent Components Estimation]","anchor":"nice-non-linear-independent-components-estimation","htmlText":"[NICE: Non-linear Independent Components Estimation]"},{"level":5,"text":"[Human-level control through deep reinforcement learning]","anchor":"human-level-control-through-deep-reinforcement-learning","htmlText":"[Human-level control through deep reinforcement learning]"},{"level":5,"text":"[Deep Unsupervised Learning using Nonequilibrium Thermodynamics]","anchor":"deep-unsupervised-learning-using-nonequilibrium-thermodynamics","htmlText":"[Deep Unsupervised Learning using Nonequilibrium Thermodynamics]"},{"level":5,"text":"[Variational Inference with Normalizing Flows]","anchor":"variational-inference-with-normalizing-flows","htmlText":"[Variational Inference with Normalizing Flows]"},{"level":5,"text":"[Deep Reinforcement Learning with Double Q-learning]","anchor":"deep-reinforcement-learning-with-double-q-learning","htmlText":"[Deep Reinforcement Learning with Double Q-learning]"},{"level":5,"text":"[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks]","anchor":"unsupervised-representation-learning-with-deep-convolutional-generative-adversarial-networks","htmlText":"[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks]"},{"level":5,"text":"[Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)]","anchor":"fast-and-accurate-deep-network-learning-by-exponential-linear-units-elus","htmlText":"[Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)]"},{"level":5,"text":"[Adversarially Learned Inference]","anchor":"adversarially-learned-inference","htmlText":"[Adversarially Learned Inference]"},{"level":5,"text":"[Improved Techniques for Training GANs]","anchor":"improved-techniques-for-training-gans","htmlText":"[Improved Techniques for Training GANs]"},{"level":5,"text":"[Gaussian Error Linear Units (GELUs)]","anchor":"gaussian-error-linear-units-gelus","htmlText":"[Gaussian Error Linear Units (GELUs)]"},{"level":5,"text":"[Least Squares Generative Adversarial Networks]","anchor":"least-squares-generative-adversarial-networks","htmlText":"[Least Squares Generative Adversarial Networks]"},{"level":5,"text":"[Image-to-Image Translation with Conditional Adversarial 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src="https://camo.githubusercontent.com/28f4d479bf0a9b033b3a3b95ab2adc343da448a025b01aefdc0fbc7f0e169eb8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d677265656e2e737667" alt="License: MIT" data-canonical-src="https://img.shields.io/badge/License-MIT-green.svg" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Papers in 100 Lines of Code</h1><a id="user-content-papers-in-100-lines-of-code" class="anchor" aria-label="Permalink: Papers in 100 Lines of Code" href="#papers-in-100-lines-of-code"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Implementation of papers in 100 lines of code.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Implemented papers</h2><a id="user-content-implemented-papers" class="anchor" aria-label="Permalink: Implemented papers" href="#implemented-papers"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Maxout Networks]</h5><a id="user-content-maxout-networks" class="anchor" aria-label="Permalink: [Maxout Networks]" href="#maxout-networks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Maxout Networks <a href="https://arxiv.org/abs/1302.4389" rel="nofollow">[arXiv]</a></li> <li><em>Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua Bengio</em></li> <li><code>2013-02-18</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Network In Network]</h5><a id="user-content-network-in-network" class="anchor" aria-label="Permalink: [Network In Network]" href="#network-in-network"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Network In Network <a href="https://arxiv.org/abs/1312.4400" rel="nofollow">[arXiv]</a></li> <li><em>Min Lin, Qiang Chen, Shuicheng Yan</em></li> <li><code>2013-12-13</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Playing Atari with Deep Reinforcement Learning]</h5><a id="user-content-playing-atari-with-deep-reinforcement-learning" class="anchor" aria-label="Permalink: [Playing Atari with Deep Reinforcement Learning]" href="#playing-atari-with-deep-reinforcement-learning"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Playing Atari with Deep Reinforcement Learning <a href="https://arxiv.org/abs/1312.5602" rel="nofollow">[arXiv]</a></li> <li><em>Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller</em></li> <li><code>2013-12-19</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Auto-Encoding Variational Bayes]</h5><a id="user-content-auto-encoding-variational-bayes" class="anchor" aria-label="Permalink: [Auto-Encoding Variational Bayes]" href="#auto-encoding-variational-bayes"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Auto-Encoding Variational Bayes <a href="https://arxiv.org/abs/1312.6114" rel="nofollow">[arXiv]</a></li> <li><em>Diederik P Kingma, Max Welling</em></li> <li><code>2013-12-20</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Generative Adversarial Networks]</h5><a id="user-content-generative-adversarial-networks" class="anchor" aria-label="Permalink: [Generative Adversarial Networks]" href="#generative-adversarial-networks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Generative Adversarial Networks <a href="https://arxiv.org/abs/1406.2661" rel="nofollow">[arXiv]</a></li> <li><em>Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio</em></li> <li><code>2014-06-10</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Conditional Generative Adversarial Nets]</h5><a id="user-content-conditional-generative-adversarial-nets" class="anchor" aria-label="Permalink: [Conditional Generative Adversarial Nets]" href="#conditional-generative-adversarial-nets"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Conditional Generative Adversarial Nets <a href="https://arxiv.org/abs/1411.1784" rel="nofollow">[arXiv]</a></li> <li><em>Mehdi Mirza, Simon Osindero</em></li> <li><code>2014-11-06</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Adam: A Method for Stochastic Optimization]</h5><a id="user-content-adam-a-method-for-stochastic-optimization" class="anchor" aria-label="Permalink: [Adam: A Method for Stochastic Optimization]" href="#adam-a-method-for-stochastic-optimization"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Adam: A Method for Stochastic Optimization <a href="https://arxiv.org/abs/1412.6980" rel="nofollow">[arXiv]</a></li> <li><em>Diederik P. Kingma, Jimmy Ba</em></li> <li><code>2014-12-22</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[NICE: Non-linear Independent Components Estimation]</h5><a id="user-content-nice-non-linear-independent-components-estimation" class="anchor" aria-label="Permalink: [NICE: Non-linear Independent Components Estimation]" href="#nice-non-linear-independent-components-estimation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>NICE: Non-linear Independent Components Estimation <a href="https://arxiv.org/abs/1410.8516" rel="nofollow">[arXiv]</a></li> <li><em>Laurent Dinh, David Krueger, Yoshua Bengio</em></li> <li><code>2014-10-30</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Human-level control through deep reinforcement learning]</h5><a id="user-content-human-level-control-through-deep-reinforcement-learning" class="anchor" aria-label="Permalink: [Human-level control through deep reinforcement learning]" href="#human-level-control-through-deep-reinforcement-learning"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Human-level control through deep reinforcement learning <a href="https://www.nature.com/articles/nature14236" rel="nofollow">[nature]</a></li> <li><em>Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg & Demis Hassabis</em></li> <li><code>2015-02-25</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Deep Unsupervised Learning using Nonequilibrium Thermodynamics]</h5><a id="user-content-deep-unsupervised-learning-using-nonequilibrium-thermodynamics" class="anchor" aria-label="Permalink: [Deep Unsupervised Learning using Nonequilibrium Thermodynamics]" href="#deep-unsupervised-learning-using-nonequilibrium-thermodynamics"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Deep Unsupervised Learning using Nonequilibrium Thermodynamics <a href="https://arxiv.org/abs/1503.03585" rel="nofollow">[arXiv]</a></li> <li><em>Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli</em></li> <li><code>2015-03-12</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Variational Inference with Normalizing Flows]</h5><a id="user-content-variational-inference-with-normalizing-flows" class="anchor" aria-label="Permalink: [Variational Inference with Normalizing Flows]" href="#variational-inference-with-normalizing-flows"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Variational Inference with Normalizing Flows <a href="https://arxiv.org/abs/1505.05770" rel="nofollow">[arXiv]</a></li> <li><em>Danilo Jimenez Rezende, Shakir Mohamed</em></li> <li><code>2015-05-21</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Deep Reinforcement Learning with Double Q-learning]</h5><a id="user-content-deep-reinforcement-learning-with-double-q-learning" class="anchor" aria-label="Permalink: [Deep Reinforcement Learning with Double Q-learning]" href="#deep-reinforcement-learning-with-double-q-learning"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Deep Reinforcement Learning with Double Q-learning <a href="https://arxiv.org/abs/1509.06461" rel="nofollow">[arXiv]</a></li> <li><em>Hado van Hasselt, Arthur Guez, David Silver</em></li> <li><code>2015-09-22</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks]</h5><a id="user-content-unsupervised-representation-learning-with-deep-convolutional-generative-adversarial-networks" class="anchor" aria-label="Permalink: [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks]" href="#unsupervised-representation-learning-with-deep-convolutional-generative-adversarial-networks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Convolutional Generative Adversarial Networks <a href="https://arxiv.org/abs/1511.06434" rel="nofollow">[arXiv]</a></li> <li><em>Alec Radford, Luke Metz, Soumith Chintala</em></li> <li><code>2015-11-19</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)]</h5><a id="user-content-fast-and-accurate-deep-network-learning-by-exponential-linear-units-elus" class="anchor" aria-label="Permalink: [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)]" href="#fast-and-accurate-deep-network-learning-by-exponential-linear-units-elus"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) <a href="https://arxiv.org/abs/1511.07289" rel="nofollow">[arXiv]</a></li> <li><em>Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter</em></li> <li><code>2015-11-23</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Adversarially Learned Inference]</h5><a id="user-content-adversarially-learned-inference" class="anchor" aria-label="Permalink: [Adversarially Learned Inference]" href="#adversarially-learned-inference"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Adversarially Learned Inference <a href="https://arxiv.org/abs/1606.00704" rel="nofollow">[arXiv]</a></li> <li><em>Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, Aaron Courville</em></li> <li><code>2016-06-02</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Improved Techniques for Training GANs]</h5><a id="user-content-improved-techniques-for-training-gans" class="anchor" aria-label="Permalink: [Improved Techniques for Training GANs]" href="#improved-techniques-for-training-gans"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Improved Techniques for Training GANs <a href="https://arxiv.org/abs/1606.03498" rel="nofollow">[arXiv]</a></li> <li><em>Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen</em></li> <li><code>2016-06-10</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Gaussian Error Linear Units (GELUs)]</h5><a id="user-content-gaussian-error-linear-units-gelus" class="anchor" aria-label="Permalink: [Gaussian Error Linear Units (GELUs)]" href="#gaussian-error-linear-units-gelus"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Gaussian Error Linear Units (GELUs) <a href="https://arxiv.org/abs/1606.08415" rel="nofollow">[arXiv]</a></li> <li><em>Dan Hendrycks, Kevin Gimpel</em></li> <li><code>2016-06-27</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Least Squares Generative Adversarial Networks]</h5><a id="user-content-least-squares-generative-adversarial-networks" class="anchor" aria-label="Permalink: [Least Squares Generative Adversarial Networks]" href="#least-squares-generative-adversarial-networks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Least Squares Generative Adversarial Networks <a href="https://arxiv.org/abs/1611.04076" rel="nofollow">[arXiv]</a></li> <li><em>Xudong Mao, Qing Li, Haoran Xie, Raymond Y.K. Lau, Zhen Wang, Stephen Paul Smolley</em></li> <li><code>2016-11-13</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Image-to-Image Translation with Conditional Adversarial Networks]</h5><a id="user-content-image-to-image-translation-with-conditional-adversarial-networks" class="anchor" aria-label="Permalink: [Image-to-Image Translation with Conditional Adversarial Networks]" href="#image-to-image-translation-with-conditional-adversarial-networks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Image-to-Image Translation with Conditional Adversarial Networks <a href="https://arxiv.org/abs/1611.07004" rel="nofollow">[arXiv]</a></li> <li><em>Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros</em></li> <li><code>2016-11-21</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Wasserstein GAN]</h5><a id="user-content-wasserstein-gan" class="anchor" aria-label="Permalink: [Wasserstein GAN]" href="#wasserstein-gan"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Wasserstein GAN <a href="https://arxiv.org/abs/1701.07875" rel="nofollow">[arXiv]</a></li> <li><em>Martin Arjovsky, Soumith Chintala, Léon Bottou</em></li> <li><code>2017-01-26</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks]</h5><a id="user-content-model-agnostic-meta-learning-for-fast-adaptation-of-deep-networks" class="anchor" aria-label="Permalink: [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks]" href="#model-agnostic-meta-learning-for-fast-adaptation-of-deep-networks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks <a href="https://arxiv.org/abs/1703.03400" rel="nofollow">[arXiv]</a></li> <li><em>Chelsea Finn, Pieter Abbeel, Sergey Levine</em></li> <li><code>2017-03-09</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks]</h5><a id="user-content-unpaired-image-to-image-translation-using-cycle-consistent-adversarial-networks" class="anchor" aria-label="Permalink: [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks]" href="#unpaired-image-to-image-translation-using-cycle-consistent-adversarial-networks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks <a href="https://arxiv.org/abs/1703.10593" rel="nofollow">[arXiv]</a></li> <li><em>Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros</em></li> <li><code>2017-03-30</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Improved Training of Wasserstein GANs]</h5><a id="user-content-improved-training-of-wasserstein-gans" class="anchor" aria-label="Permalink: [Improved Training of Wasserstein GANs]" href="#improved-training-of-wasserstein-gans"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Improved Training of Wasserstein GANs <a href="https://arxiv.org/abs/1704.00028" rel="nofollow">[arXiv]</a></li> <li><em>Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron Courville</em></li> <li><code>2017-03-31</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Adversarial Feature Learning]</h5><a id="user-content-adversarial-feature-learning" class="anchor" aria-label="Permalink: [Adversarial Feature Learning]" href="#adversarial-feature-learning"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Adversarial Feature Learning <a href="https://arxiv.org/abs/1605.09782" rel="nofollow">[arXiv]</a></li> <li><em>Jeff Donahue, Philipp Krähenbühl, Trevor Darrell</em></li> <li><code>2017-04-03</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Self-Normalizing Neural Networks]</h5><a id="user-content-self-normalizing-neural-networks" class="anchor" aria-label="Permalink: [Self-Normalizing Neural Networks]" href="#self-normalizing-neural-networks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Self-Normalizing Neural Networks <a href="https://arxiv.org/abs/1706.02515" rel="nofollow">[arXiv]</a></li> <li><em>Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter</em></li> <li><code>2017-06-08</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Proximal Policy Optimization Algorithms]</h5><a id="user-content-proximal-policy-optimization-algorithms" class="anchor" aria-label="Permalink: [Proximal Policy Optimization Algorithms]" href="#proximal-policy-optimization-algorithms"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Proximal Policy Optimization Algorithms <a href="https://arxiv.org/abs/1707.06347" rel="nofollow">[arXiv]</a></li> <li><em>John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov</em></li> <li><code>2017-08-28</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Deep Image Prior]</h5><a id="user-content-deep-image-prior" class="anchor" aria-label="Permalink: [Deep Image Prior]" href="#deep-image-prior"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Deep Image Prior <a href="https://arxiv.org/abs/1711.10925" rel="nofollow">[arXiv]</a></li> <li><em>Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky</em></li> <li><code>2017-11-29</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[On First-Order Meta-Learning Algorithms]</h5><a id="user-content-on-first-order-meta-learning-algorithms" class="anchor" aria-label="Permalink: [On First-Order Meta-Learning Algorithms]" href="#on-first-order-meta-learning-algorithms"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>On First-Order Meta-Learning Algorithms <a href="https://arxiv.org/abs/1803.02999" rel="nofollow">[arXiv]</a></li> <li><em>Alex Nichol, Joshua Achiam, John Schulman</em></li> <li><code>2018-03-08</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Sequential Neural Likelihood]</h5><a id="user-content-sequential-neural-likelihood" class="anchor" aria-label="Permalink: [Sequential Neural Likelihood]" href="#sequential-neural-likelihood"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows <a href="https://arxiv.org/abs/1805.07226" rel="nofollow">[arXiv]</a></li> <li><em>George Papamakarios, David C. Sterratt, Iain Murray</em></li> <li><code>2018-05-18</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[On the Variance of the Adaptive Learning Rate and Beyond]</h5><a id="user-content-on-the-variance-of-the-adaptive-learning-rate-and-beyond" class="anchor" aria-label="Permalink: [On the Variance of the Adaptive Learning Rate and Beyond]" href="#on-the-variance-of-the-adaptive-learning-rate-and-beyond"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>On the Variance of the Adaptive Learning Rate and Beyond <a href="https://arxiv.org/abs/1908.03265" rel="nofollow">[arXiv]</a></li> <li><em>Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, Jiawei Han</em></li> <li><code>2019-08-08</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Optimizing Millions of Hyperparameters by Implicit Differentiation]</h5><a id="user-content-optimizing-millions-of-hyperparameters-by-implicit-differentiation" class="anchor" aria-label="Permalink: [Optimizing Millions of Hyperparameters by Implicit Differentiation]" href="#optimizing-millions-of-hyperparameters-by-implicit-differentiation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Optimizing Millions of Hyperparameters by Implicit Differentiation <a href="https://proceedings.mlr.press/v108/lorraine20a" rel="nofollow">[PMLR]</a></li> <li><em>Jonathan Lorraine, Paul Vicol, David Duvenaud</em></li> <li><code>2019-10-06</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Implicit Neural Representations with Periodic Activation Functions]</h5><a id="user-content-implicit-neural-representations-with-periodic-activation-functions" class="anchor" aria-label="Permalink: [Implicit Neural Representations with Periodic Activation Functions]" href="#implicit-neural-representations-with-periodic-activation-functions"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Implicit Neural Representations with Periodic Activation Functions <a href="https://arxiv.org/abs/2006.09661" rel="nofollow">[arXiv]</a></li> <li><em>Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, Gordon Wetzstein</em></li> <li><code>2020-06-17</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains]</h5><a id="user-content-fourier-features-let-networks-learn-high-frequency-functions-in-low-dimensional-domains" class="anchor" aria-label="Permalink: [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains]" href="#fourier-features-let-networks-learn-high-frequency-functions-in-low-dimensional-domains"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains <a href="https://arxiv.org/abs/2006.10739" rel="nofollow">[arXiv]</a></li> <li><em>Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, Ren Ng</em></li> <li><code>2020-06-18</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Denoising Diffusion Probabilistic Models]</h5><a id="user-content-denoising-diffusion-probabilistic-models" class="anchor" aria-label="Permalink: [Denoising Diffusion Probabilistic Models]" href="#denoising-diffusion-probabilistic-models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Denoising Diffusion Probabilistic Models <a href="https://arxiv.org/abs/2006.11239" rel="nofollow">[arXiv]</a></li> <li><em>Jonathan Ho, Ajay Jain, Pieter Abbeel</em></li> <li><code>2020-06-19</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Likelihood-free MCMC with Amortized Approximate Ratio Estimators]</h5><a id="user-content-likelihood-free-mcmc-with-amortized-approximate-ratio-estimators" class="anchor" aria-label="Permalink: [Likelihood-free MCMC with Amortized Approximate Ratio Estimators]" href="#likelihood-free-mcmc-with-amortized-approximate-ratio-estimators"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Likelihood-free MCMC with Amortized Approximate Ratio Estimators <a href="http://proceedings.mlr.press/v108/lorraine20a" rel="nofollow">[PMLR]</a></li> <li><em>Joeri Hermans, Volodimir Begy, Gilles Louppe</em></li> <li><code>2020-06-26</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis]</h5><a id="user-content-nerf-representing-scenes-as-neural-radiance-fields-for-view-synthesis" class="anchor" aria-label="Permalink: [NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis]" href="#nerf-representing-scenes-as-neural-radiance-fields-for-view-synthesis"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis <a href="https://arxiv.org/abs/2003.08934" rel="nofollow">[arXiv]</a></li> <li><em>Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng</em></li> <li><code>2020-08-03</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Multiplicative Filter Networks]</h5><a id="user-content-multiplicative-filter-networks" class="anchor" aria-label="Permalink: [Multiplicative Filter Networks]" href="#multiplicative-filter-networks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Multiplicative Filter Networks <a href="https://openreview.net/forum?id=OmtmcPkkhT" rel="nofollow">[OpenReview]</a></li> <li><em>Rizal Fathony, Anit Kumar Sahu, Devin Willmott, J Zico Kolter</em></li> <li><code>2020-09-28</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Learned Initializations for Optimizing Coordinate-Based Neural Representations]</h5><a id="user-content-learned-initializations-for-optimizing-coordinate-based-neural-representations" class="anchor" aria-label="Permalink: [Learned Initializations for Optimizing Coordinate-Based Neural Representations]" href="#learned-initializations-for-optimizing-coordinate-based-neural-representations"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Learned Initializations for Optimizing Coordinate-Based Neural Representations <a href="https://arxiv.org/abs/2012.02189" rel="nofollow">[arXiv]</a></li> <li><em>Matthew Tancik, Ben Mildenhall, Terrance Wang, Divi Schmidt, Pratul P. Srinivasan, Jonathan T. Barron, Ren Ng</em></li> <li><code>2020-12-03</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[FastNeRF: High-Fidelity Neural Rendering at 200FPS]</h5><a id="user-content-fastnerf-high-fidelity-neural-rendering-at-200fps" class="anchor" aria-label="Permalink: [FastNeRF: High-Fidelity Neural Rendering at 200FPS]" href="#fastnerf-high-fidelity-neural-rendering-at-200fps"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>FastNeRF: High-Fidelity Neural Rendering at 200FPS <a href="https://arxiv.org/abs/2103.10380" rel="nofollow">[arXiv]</a></li> <li><em>Stephan J. Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, Julien Valentin</em></li> <li><code>2021-03-18</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs]</h5><a id="user-content-kilonerf-speeding-up-neural-radiance-fields-with-thousands-of-tiny-mlps" class="anchor" aria-label="Permalink: [KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs]" href="#kilonerf-speeding-up-neural-radiance-fields-with-thousands-of-tiny-mlps"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs <a href="https://arxiv.org/abs/2103.13744" rel="nofollow">[arXiv]</a></li> <li><em>Christian Reiser, Songyou Peng, Yiyi Liao, Andreas Geiger</em></li> <li><code>2021-03-25</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[PlenOctrees for Real-time Rendering of Neural Radiance Fields]</h5><a id="user-content-plenoctrees-for-real-time-rendering-of-neural-radiance-fields" class="anchor" aria-label="Permalink: [PlenOctrees for Real-time Rendering of Neural Radiance Fields]" href="#plenoctrees-for-real-time-rendering-of-neural-radiance-fields"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>PlenOctrees for Real-time Rendering of Neural Radiance Fields <a href="https://arxiv.org/abs/2103.14024" rel="nofollow">[arXiv]</a></li> <li><em>Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, Angjoo Kanazawa</em></li> <li><code>2021-03-25</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[NeRF--: Neural Radiance Fields Without Known Camera Parameters]</h5><a id="user-content-nerf---neural-radiance-fields-without-known-camera-parameters" class="anchor" aria-label="Permalink: [NeRF--: Neural Radiance Fields Without Known Camera Parameters]" href="#nerf---neural-radiance-fields-without-known-camera-parameters"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>NeRF--: Neural Radiance Fields Without Known Camera Parameters <a href="https://arxiv.org/abs/2102.07064" rel="nofollow">[arXiv]</a></li> <li><em>Zirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, Victor Adrian Prisacariu</em></li> <li><code>2021-02-14</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Gromov-Wasserstein Distances between Gaussian Distributions]</h5><a id="user-content-gromov-wasserstein-distances-between-gaussian-distributions" class="anchor" aria-label="Permalink: [Gromov-Wasserstein Distances between Gaussian Distributions]" href="#gromov-wasserstein-distances-between-gaussian-distributions"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Gromov-Wasserstein Distances between Gaussian Distributions <a href="https://arxiv.org/abs/2104.07970" rel="nofollow">[arXiv]</a></li> <li><em>Antoine Salmona, Julie Delon, Agnès Desolneux</em></li> <li><code>2021-08-16</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Plenoxels: Radiance Fields without Neural Networks]</h5><a id="user-content-plenoxels-radiance-fields-without-neural-networks" class="anchor" aria-label="Permalink: [Plenoxels: Radiance Fields without Neural Networks]" href="#plenoxels-radiance-fields-without-neural-networks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Plenoxels: Radiance Fields without Neural Networks <a href="https://arxiv.org/abs/2112.05131" rel="nofollow">[arXiv]</a></li> <li><em>Alex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa</em></li> <li><code>2021-12-09</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering]</h5><a id="user-content-infonerf-ray-entropy-minimization-for-few-shot-neural-volume-rendering" class="anchor" aria-label="Permalink: [InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering]" href="#infonerf-ray-entropy-minimization-for-few-shot-neural-volume-rendering"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering <a href="https://arxiv.org/abs/2112.15399" rel="nofollow">[arXiv]</a></li> <li><em>Mijeong Kim, Seonguk Seo, Bohyung Han</em></li> <li><code>2021-12-31</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Instant Neural Graphics Primitives with a Multiresolution Hash Encoding]</h5><a id="user-content-instant-neural-graphics-primitives-with-a-multiresolution-hash-encoding" class="anchor" aria-label="Permalink: [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding]" href="#instant-neural-graphics-primitives-with-a-multiresolution-hash-encoding"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Instant Neural Graphics Primitives with a Multiresolution Hash Encoding <a href="https://arxiv.org/abs/2201.05989" rel="nofollow">[arXiv]</a></li> <li><em>Thomas Müller, Alex Evans, Christoph Schied, Alexander Keller</em></li> <li><code>2022-01-16</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow]</h5><a id="user-content-flow-straight-and-fast-learning-to-generate-and-transfer-data-with-rectified-flow" class="anchor" aria-label="Permalink: [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow]" href="#flow-straight-and-fast-learning-to-generate-and-transfer-data-with-rectified-flow"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow <a href="https://arxiv.org/abs/2209.03003" rel="nofollow">[arXiv]</a></li> <li><em>Xingchao Liu, Chengyue Gong, Qiang Liu</em></li> <li><code>2022-09-07</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[K-Planes: Explicit Radiance Fields in Space, Time, and Appearance]</h5><a id="user-content-k-planes-explicit-radiance-fields-in-space-time-and-appearance" class="anchor" aria-label="Permalink: [K-Planes: Explicit Radiance Fields in Space, Time, and Appearance]" href="#k-planes-explicit-radiance-fields-in-space-time-and-appearance"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>K-Planes: Explicit Radiance Fields in Space, Time, and Appearance <a href="https://arxiv.org/abs/2301.10241" rel="nofollow">[arXiv]</a></li> <li><em>Sara Fridovich-Keil, Giacomo Meanti, Frederik Warburg, Benjamin Recht, Angjoo Kanazawa</em></li> <li><code>2023-01-24</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">[FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization]</h5><a id="user-content-freenerf-improving-few-shot-neural-rendering-with-free-frequency-regularization" class="anchor" aria-label="Permalink: [FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization]" href="#freenerf-improving-few-shot-neural-rendering-with-free-frequency-regularization"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization <a href="https://arxiv.org/abs/2303.07418" rel="nofollow">[arXiv]</a></li> <li><em>Jiawei Yang, Marco Pavone, Yue Wang</em></li> <li><code>2023-03-13</code></li> </ul> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="mloqxy1ti+LTWUBpdNXatKhgmqeMASRpqcYFaN/vu6HVg8o7q7/XMcQ7J/CzRimXFCbkGeFUDty8qdUVusunig==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> Implementation of papers in 100 lines of code. </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/python" title="Topic: python" data-view-component="true" class="topic-tag topic-tag-link"> python </a> <a href="/topics/machine-learning" title="Topic: machine-learning" data-view-component="true" class="topic-tag topic-tag-link"> machine-learning </a> <a href="/topics/research" title="Topic: research" data-view-component="true" class="topic-tag topic-tag-link"> research </a> <a href="/topics/reinforcement-learning" title="Topic: reinforcement-learning" data-view-component="true" class="topic-tag topic-tag-link"> reinforcement-learning </a> <a href="/topics/deep-learning" title="Topic: deep-learning" data-view-component="true" class="topic-tag topic-tag-link"> deep-learning </a> <a href="/topics/aes" title="Topic: aes" data-view-component="true" class="topic-tag topic-tag-link"> aes </a> <a href="/topics/pytorch" title="Topic: pytorch" data-view-component="true" class="topic-tag topic-tag-link"> pytorch </a> <a href="/topics/artificial-intelligence" title="Topic: artificial-intelligence" data-view-component="true" class="topic-tag topic-tag-link"> artificial-intelligence </a> <a 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