Machine Learning Jan 2019

<!DOCTYPE html> <html lang="en"> <head> <title>Machine Learning Jan 2019</title> <meta name="viewport" content="width=device-width, initial-scale=1"> <link rel="apple-touch-icon" sizes="180x180" href="/static/browse/0.3.4/images/icons/apple-touch-icon.png"> <link rel="icon" type="image/png" sizes="32x32" href="/static/browse/0.3.4/images/icons/favicon-32x32.png"> <link rel="icon" type="image/png" sizes="16x16" href="/static/browse/0.3.4/images/icons/favicon-16x16.png"> <link rel="manifest" href="/static/browse/0.3.4/images/icons/site.webmanifest"> <link rel="mask-icon" href="/static/browse/0.3.4/images/icons/safari-pinned-tab.svg" color="#5bbad5"> <meta name="msapplication-TileColor" content="#da532c"> <meta name="theme-color" content="#ffffff"> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/arXiv.css?v=20241206" /> <link rel="stylesheet" type="text/css" media="print" href="/static/browse/0.3.4/css/arXiv-print.css?v=20200611" /> <link 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href=/list/cs.LG/2019-01?skip=100&show=50>101-150</a> <a href=/list/cs.LG/2019-01?skip=150&show=50>151-200</a> <span>...</span> <a href=/list/cs.LG/2019-01?skip=1050&show=50>1051-1100</a> </div> <div class='morefewer'>Showing up to 50 entries per page: <a href=/list/cs.LG/2019-01?skip=0&show=25 rel="nofollow"> fewer</a> | <a href=/list/cs.LG/2019-01?skip=0&show=100 rel="nofollow"> more</a> | <a href=/list/cs.LG/2019-01?skip=0&show=2000 rel="nofollow"> all</a> </div> <dl id='articles'> <dt> <a name='item1'>[1]</a> <a href ="/abs/1901.00035" title="Abstract" id="1901.00035"> arXiv:1901.00035 </a> [<a href="/pdf/1901.00035" title="Download PDF" id="pdf-1901.00035" aria-labelledby="pdf-1901.00035">pdf</a>, <a href="/format/1901.00035" title="Other formats" id="oth-1901.00035" aria-labelledby="oth-1901.00035">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Convex Relaxations of Convolutional Neural Nets </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Bartan,+B">Burak Bartan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pilanci,+M">Mert Pilanci</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item2'>[2]</a> <a href ="/abs/1901.00059" title="Abstract" id="1901.00059"> arXiv:1901.00059 </a> [<a href="/pdf/1901.00059" title="Download PDF" id="pdf-1901.00059" aria-labelledby="pdf-1901.00059">pdf</a>, <a href="/format/1901.00059" title="Other formats" id="oth-1901.00059" aria-labelledby="oth-1901.00059">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Determining Principal Component Cardinality through the Principle of Minimum Description Length </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Tavory,+A">Ami Tavory</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> LOD 2019 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item3'>[3]</a> <a href ="/abs/1901.00069" title="Abstract" id="1901.00069"> arXiv:1901.00069 </a> [<a href="/pdf/1901.00069" title="Download PDF" id="pdf-1901.00069" aria-labelledby="pdf-1901.00069">pdf</a>, <a href="/format/1901.00069" title="Other formats" id="oth-1901.00069" aria-labelledby="oth-1901.00069">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Recurrent Neural Networks for Time Series Forecasting </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Petneh%C3%A1zi,+G">G谩bor Petneh谩zi</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item4'>[4]</a> <a href ="/abs/1901.00072" title="Abstract" id="1901.00072"> arXiv:1901.00072 </a> [<a href="/pdf/1901.00072" title="Download PDF" id="pdf-1901.00072" aria-labelledby="pdf-1901.00072">pdf</a>, <a href="/format/1901.00072" title="Other formats" id="oth-1901.00072" aria-labelledby="oth-1901.00072">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Exploring spectro-temporal features in end-to-end convolutional neural networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Robertson,+S">Sean Robertson</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Penn,+G">Gerald Penn</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y">Yingxue Wang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computation and Language (cs.CL); Sound (cs.SD); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item5'>[5]</a> <a href ="/abs/1901.00109" title="Abstract" id="1901.00109"> arXiv:1901.00109 </a> [<a href="/pdf/1901.00109" title="Download PDF" id="pdf-1901.00109" aria-labelledby="pdf-1901.00109">pdf</a>, <a href="/format/1901.00109" title="Other formats" id="oth-1901.00109" aria-labelledby="oth-1901.00109">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Morphological Network: How Far Can We Go with Morphological Neurons? </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Mondal,+R">Ranjan Mondal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Santra,+S">Sanchayan Santra</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mukherjee,+S+S">Soumendu Sundar Mukherjee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chanda,+B">Bhabatosh Chanda</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted at BMVC 2022 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item6'>[6]</a> <a href ="/abs/1901.00117" title="Abstract" id="1901.00117"> arXiv:1901.00117 </a> [<a href="/pdf/1901.00117" title="Download PDF" id="pdf-1901.00117" aria-labelledby="pdf-1901.00117">pdf</a>, <a href="/format/1901.00117" title="Other formats" id="oth-1901.00117" aria-labelledby="oth-1901.00117">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> An Active Learning Framework for Efficient Robust Policy Search </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Narayanaswami,+S+K">Sai Kiran Narayanaswami</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sudarsanam,+N">Nandan Sudarsanam</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ravindran,+B">Balaraman Ravindran</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 9 pages, 6 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Robotics (cs.RO); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item7'>[7]</a> <a href ="/abs/1901.00130" title="Abstract" id="1901.00130"> arXiv:1901.00130 </a> [<a href="/pdf/1901.00130" title="Download PDF" id="pdf-1901.00130" aria-labelledby="pdf-1901.00130">pdf</a>, <a href="/format/1901.00130" title="Other formats" id="oth-1901.00130" aria-labelledby="oth-1901.00130">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Realizing data features by deep nets </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Guo,+Z">Zheng-Chu Guo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shi,+L">Lei Shi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lin,+S">Shao-Bo Lin</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 12 pages, 2 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item8'>[8]</a> <a href ="/abs/1901.00137" title="Abstract" id="1901.00137"> arXiv:1901.00137 </a> [<a href="/pdf/1901.00137" title="Download PDF" id="pdf-1901.00137" aria-labelledby="pdf-1901.00137">pdf</a>, <a href="/format/1901.00137" title="Other formats" id="oth-1901.00137" aria-labelledby="oth-1901.00137">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Theoretical Analysis of Deep Q-Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Fan,+J">Jianqing Fan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Z">Zhaoran Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xie,+Y">Yuchen Xie</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+Z">Zhuoran Yang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 65 pages </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Optimization and Control (math.OC); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item9'>[9]</a> <a href ="/abs/1901.00172" title="Abstract" id="1901.00172"> arXiv:1901.00172 </a> [<a href="/pdf/1901.00172" title="Download PDF" id="pdf-1901.00172" aria-labelledby="pdf-1901.00172">pdf</a>, <a href="/format/1901.00172" title="Other formats" id="oth-1901.00172" aria-labelledby="oth-1901.00172">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Supervised Multiscale Dimension Reduction for Spatial Interaction Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Han,+S">Shaobo Han</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dunson,+D+B">David B. Dunson</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 30 pages, 12 figures, revised for clarity and conciseness </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Social and Information Networks (cs.SI); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item10'>[10]</a> <a href ="/abs/1901.00188" title="Abstract" id="1901.00188"> arXiv:1901.00188 </a> [<a href="/pdf/1901.00188" title="Download PDF" id="pdf-1901.00188" aria-labelledby="pdf-1901.00188">pdf</a>, <a href="/format/1901.00188" title="Other formats" id="oth-1901.00188" aria-labelledby="oth-1901.00188">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Complementary reinforcement learning towards explainable agents </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+J+H">Jung Hoon Lee</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 14 pages, 5 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item11'>[11]</a> <a href ="/abs/1901.00210" title="Abstract" id="1901.00210"> arXiv:1901.00210 </a> [<a href="/pdf/1901.00210" title="Download PDF" id="pdf-1901.00210" aria-labelledby="pdf-1901.00210">pdf</a>, <a href="/format/1901.00210" title="Other formats" id="oth-1901.00210" aria-labelledby="oth-1901.00210">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zanette,+A">Andrea Zanette</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Brunskill,+E">Emma Brunskill</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Bug fixes </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> International Conference on Machine Learning 2019 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item12'>[12]</a> <a href ="/abs/1901.00214" title="Abstract" id="1901.00214"> arXiv:1901.00214 </a> [<a href="/pdf/1901.00214" title="Download PDF" id="pdf-1901.00214" aria-labelledby="pdf-1901.00214">pdf</a>, <a href="/format/1901.00214" title="Other formats" id="oth-1901.00214" aria-labelledby="oth-1901.00214">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Clustering with Distributed Data </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kar,+S">Soummya Kar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Swenson,+B">Brian Swenson</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Optimization and Control (math.OC); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item13'>[13]</a> <a href ="/abs/1901.00243" title="Abstract" id="1901.00243"> arXiv:1901.00243 </a> [<a href="/pdf/1901.00243" title="Download PDF" id="pdf-1901.00243" aria-labelledby="pdf-1901.00243">pdf</a>, <a href="/format/1901.00243" title="Other formats" id="oth-1901.00243" aria-labelledby="oth-1901.00243">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kachuee,+M">Mohammad Kachuee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Goldstein,+O">Orpaz Goldstein</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Karkkainen,+K">Kimmo Karkkainen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Darabi,+S">Sajad Darabi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sarrafzadeh,+M">Majid Sarrafzadeh</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> <a href="https://openreview.net/forum?id=S1eOHo09KX" rel="external noopener nofollow" class="link-external link-https">this https URL</a> </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> International Conference on Learning Representations (ICLR), 2019 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item14'>[14]</a> <a href ="/abs/1901.00246" title="Abstract" id="1901.00246"> arXiv:1901.00246 </a> [<a href="/pdf/1901.00246" title="Download PDF" id="pdf-1901.00246" aria-labelledby="pdf-1901.00246">pdf</a>, <a href="/format/1901.00246" title="Other formats" id="oth-1901.00246" aria-labelledby="oth-1901.00246">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Hazard,+C+J">Christopher J. Hazard</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fusting,+C">Christopher Fusting</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Resnick,+M">Michael Resnick</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Auerbach,+M">Michael Auerbach</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Meehan,+M">Michael Meehan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Korobov,+V">Valeri Korobov</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 16 pages </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item15'>[15]</a> <a href ="/abs/1901.00248" title="Abstract" id="1901.00248"> arXiv:1901.00248 </a> [<a href="/pdf/1901.00248" title="Download PDF" id="pdf-1901.00248" aria-labelledby="pdf-1901.00248">pdf</a>, <a href="/format/1901.00248" title="Other formats" id="oth-1901.00248" aria-labelledby="oth-1901.00248">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Survey on Multi-output Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Xu,+D">Donna Xu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shi,+Y">Yaxin Shi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tsang,+I+W">Ivor W. Tsang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ong,+Y">Yew-Soon Ong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gong,+C">Chen Gong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shen,+X">Xiaobo Shen</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Paper accepted by IEEE Transactions on Neural Networks and Learning Systems </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item16'>[16]</a> <a href ="/abs/1901.00276" title="Abstract" id="1901.00276"> arXiv:1901.00276 </a> [<a href="/pdf/1901.00276" title="Download PDF" id="pdf-1901.00276" aria-labelledby="pdf-1901.00276">pdf</a>, <a href="/format/1901.00276" title="Other formats" id="oth-1901.00276" aria-labelledby="oth-1901.00276">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+M">Miao Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+H">Huiqi Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lyu,+J">Juan Lyu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ling,+S+H">Sai Ho Ling</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Su,+S">Steven Su</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Image and Video Processing (eess.IV) </div> </div> </dd> <dt> <a name='item17'>[17]</a> <a href ="/abs/1901.00279" title="Abstract" id="1901.00279"> arXiv:1901.00279 </a> [<a href="/pdf/1901.00279" title="Download PDF" id="pdf-1901.00279" aria-labelledby="pdf-1901.00279">pdf</a>, <a href="/format/1901.00279" title="Other formats" id="oth-1901.00279" aria-labelledby="oth-1901.00279">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Elimination of All Bad Local Minima in Deep Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kawaguchi,+K">Kenji Kawaguchi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kaelbling,+L+P">Leslie Pack Kaelbling</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted to appear in AISTATS 2020 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Neural and Evolutionary Computing (cs.NE); Optimization and Control (math.OC); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item18'>[18]</a> <a href ="/abs/1901.00301" title="Abstract" id="1901.00301"> arXiv:1901.00301 </a> [<a href="/pdf/1901.00301" title="Download PDF" id="pdf-1901.00301" aria-labelledby="pdf-1901.00301">pdf</a>, <a href="/format/1901.00301" title="Other formats" id="oth-1901.00301" aria-labelledby="oth-1901.00301">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+C">Chicheng Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Agarwal,+A">Alekh Agarwal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Daum%C3%A9,+H">Hal Daum茅 III</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Langford,+J">John Langford</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Negahban,+S+N">Sahand N Negahban</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 42 pages, 21 figures, ICML 2019 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item19'>[19]</a> <a href ="/abs/1901.00397" title="Abstract" id="1901.00397"> arXiv:1901.00397 </a> [<a href="/pdf/1901.00397" title="Download PDF" id="pdf-1901.00397" aria-labelledby="pdf-1901.00397">pdf</a>, <a href="/format/1901.00397" title="Other formats" id="oth-1901.00397" aria-labelledby="oth-1901.00397">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Saldias,+B">Belen Saldias</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Protopapas,+P">Pavlos Protopapas</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pichara,+K">Karim Pichara</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> SIAM International Conference on Data Mining (SDM19), 9 official pages, 5 supplementary pages </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Proceedings of the 2019 SIAM International Conference on Data Mining </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item20'>[20]</a> <a href ="/abs/1901.00434" title="Abstract" id="1901.00434"> arXiv:1901.00434 </a> [<a href="/pdf/1901.00434" title="Download PDF" id="pdf-1901.00434" aria-labelledby="pdf-1901.00434">pdf</a>, <a href="/format/1901.00434" title="Other formats" id="oth-1901.00434" aria-labelledby="oth-1901.00434">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> The capacity of feedforward neural networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Baldi,+P">Pierre Baldi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Vershynin,+R">Roman Vershynin</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 49 pages. Introduction is expanded and conclusion is added </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Neural and Evolutionary Computing (cs.NE); Combinatorics (math.CO); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item21'>[21]</a> <a href ="/abs/1901.00444" title="Abstract" id="1901.00444"> arXiv:1901.00444 </a> [<a href="/pdf/1901.00444" title="Download PDF" id="pdf-1901.00444" aria-labelledby="pdf-1901.00444">pdf</a>, <a href="/format/1901.00444" title="Other formats" id="oth-1901.00444" aria-labelledby="oth-1901.00444">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> BMF: Block matrix approach to factorization of large scale data </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Bhavana,+P+G">Prasad G Bhavana</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nair,+V+C">Vineet C Nair</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Disagreement on success criteria of the method with my guide </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item22'>[22]</a> <a href ="/abs/1901.00451" title="Abstract" id="1901.00451"> arXiv:1901.00451 </a> [<a href="/pdf/1901.00451" title="Download PDF" id="pdf-1901.00451" aria-labelledby="pdf-1901.00451">pdf</a>, <a href="/format/1901.00451" title="Other formats" id="oth-1901.00451" aria-labelledby="oth-1901.00451">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> SGD Converges to Global Minimum in Deep Learning via Star-convex Path </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+Y">Yi Zhou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+J">Junjie Yang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+H">Huishuai Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liang,+Y">Yingbin Liang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tarokh,+V">Vahid Tarokh</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> ICLR2019 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item23'>[23]</a> <a href ="/abs/1901.00461" title="Abstract" id="1901.00461"> arXiv:1901.00461 </a> [<a href="/pdf/1901.00461" title="Download PDF" id="pdf-1901.00461" aria-labelledby="pdf-1901.00461">pdf</a>, <a href="/format/1901.00461" title="Other formats" id="oth-1901.00461" aria-labelledby="oth-1901.00461">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A CNN adapted to time series for the classification of Supernovae </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Brunel,+A">Anthony Brunel</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pasquet,+J">Johanna Pasquet</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pasquet,+J">J茅r么me Pasquet</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rodriguez,+N">Nancy Rodriguez</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Comby,+F">Fr茅d茅ric Comby</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fouchez,+D">Dominique Fouchez</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chaumont,+M">Marc Chaumont</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> IS&T International Symposium on Electronic Imaging, EI'2019, Color Imaging XXIV: Displaying, Processing, Hardcopy, and Applications, Burlingame (suburb of San Francisco), California USA, 13 - 17 January, 2019, 8 pages. The CNN is downloadable there: <a href="https://github.com/Anzzy30/SupernovaeClassification" rel="external noopener nofollow" class="link-external link-https">this https URL</a> </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item24'>[24]</a> <a href ="/abs/1901.00516" title="Abstract" id="1901.00516"> arXiv:1901.00516 </a> [<a href="/pdf/1901.00516" title="Download PDF" id="pdf-1901.00516" aria-labelledby="pdf-1901.00516">pdf</a>, <a href="/format/1901.00516" title="Other formats" id="oth-1901.00516" aria-labelledby="oth-1901.00516">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Honey Authentication with Machine Learning Augmented Bright-Field Microscopy </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=He,+C">Chloe He</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gkantiragas,+A">Alexis Gkantiragas</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Glowacki,+G">Gerard Glowacki</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted at the 'AI for Social Good' workshop at the 32nd Conference on Neural Information Processing Systems (NeurIPS2018), Montr茅al, Canada </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE); Quantitative Methods (q-bio.QM) </div> </div> </dd> <dt> <a name='item25'>[25]</a> <a href ="/abs/1901.00532" title="Abstract" id="1901.00532"> arXiv:1901.00532 </a> [<a href="/pdf/1901.00532" title="Download PDF" id="pdf-1901.00532" aria-labelledby="pdf-1901.00532">pdf</a>, <a href="/format/1901.00532" title="Other formats" id="oth-1901.00532" aria-labelledby="oth-1901.00532">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Adversarial Robustness May Be at Odds With Simplicity </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Nakkiran,+P">Preetum Nakkiran</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> welcome </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computational Complexity (cs.CC); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item26'>[26]</a> <a href ="/abs/1901.00544" title="Abstract" id="1901.00544"> arXiv:1901.00544 </a> [<a href="/pdf/1901.00544" title="Download PDF" id="pdf-1901.00544" aria-labelledby="pdf-1901.00544">pdf</a>, <a href="/format/1901.00544" title="Other formats" id="oth-1901.00544" aria-labelledby="oth-1901.00544">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Multi-class Classification without Multi-class Labels </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Hsu,+Y">Yen-Chang Hsu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lv,+Z">Zhaoyang Lv</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Schlosser,+J">Joel Schlosser</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Odom,+P">Phillip Odom</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kira,+Z">Zsolt Kira</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> International Conference on Learning Representations (ICLR 2019) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item27'>[27]</a> <a href ="/abs/1901.00546" title="Abstract" id="1901.00546"> arXiv:1901.00546 </a> [<a href="/pdf/1901.00546" title="Download PDF" id="pdf-1901.00546" aria-labelledby="pdf-1901.00546">pdf</a>, <a href="/format/1901.00546" title="Other formats" id="oth-1901.00546" aria-labelledby="oth-1901.00546">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Multi-Label Adversarial Perturbations </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Song,+Q">Qingquan Song</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jin,+H">Haifeng Jin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+X">Xiao Huang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hu,+X">Xia Hu</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Cryptography and Security (cs.CR); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item28'>[28]</a> <a href ="/abs/1901.00560" title="Abstract" id="1901.00560"> arXiv:1901.00560 </a> [<a href="/pdf/1901.00560" title="Download PDF" id="pdf-1901.00560" aria-labelledby="pdf-1901.00560">pdf</a>, <a href="/format/1901.00560" title="Other formats" id="oth-1901.00560" aria-labelledby="oth-1901.00560">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Instance-Based Classification through Hypothesis Testing </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=He,+Z">Zengyou He</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sheng,+C">Chaohua Sheng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+Y">Yan Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zou,+Q">Quan Zou</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item29'>[29]</a> <a href ="/abs/1901.00569" title="Abstract" id="1901.00569"> arXiv:1901.00569 </a> [<a href="/pdf/1901.00569" title="Download PDF" id="pdf-1901.00569" aria-labelledby="pdf-1901.00569">pdf</a>, <a href="/format/1901.00569" title="Other formats" id="oth-1901.00569" aria-labelledby="oth-1901.00569">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhu,+M">Meixin Zhu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+X">Xuesong Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y">Yinhai Wang</a></div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Transportation Research Part C: Emerging Technologies 2018 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item30'>[30]</a> <a href ="/abs/1901.00578" title="Abstract" id="1901.00578"> arXiv:1901.00578 </a> [<a href="/pdf/1901.00578" title="Download PDF" id="pdf-1901.00578" aria-labelledby="pdf-1901.00578">pdf</a>, <a href="/format/1901.00578" title="Other formats" id="oth-1901.00578" aria-labelledby="oth-1901.00578">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Prediction of multi-dimensional spatial variation data via Bayesian tensor completion </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Luan,+J">Jiali Luan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Z">Zheng Zhang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Statistics Theory (math.ST); Computation (stat.CO); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item31'>[31]</a> <a href ="/abs/1901.00596" title="Abstract" id="1901.00596"> arXiv:1901.00596 </a> [<a href="/pdf/1901.00596" title="Download PDF" id="pdf-1901.00596" aria-labelledby="pdf-1901.00596">pdf</a>, <a href="/format/1901.00596" title="Other formats" id="oth-1901.00596" aria-labelledby="oth-1901.00596">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Comprehensive Survey on Graph Neural Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wu,+Z">Zonghan Wu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pan,+S">Shirui Pan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+F">Fengwen Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Long,+G">Guodong Long</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+C">Chengqi Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yu,+P+S">Philip S. Yu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Minor revision (updated tables and references) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item32'>[32]</a> <a href ="/abs/1901.00612" title="Abstract" id="1901.00612"> arXiv:1901.00612 </a> [<a href="/pdf/1901.00612" title="Download PDF" id="pdf-1901.00612" aria-labelledby="pdf-1901.00612">pdf</a>, <a href="/format/1901.00612" title="Other formats" id="oth-1901.00612" aria-labelledby="oth-1901.00612">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Adversarial Learning of a Sampler Based on an Unnormalized Distribution </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+C">Chunyuan Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bai,+K">Ke Bai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+J">Jianqiao Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+G">Guoyin Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+C">Changyou Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Carin,+L">Lawrence Carin</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Published in AISTATS 2019; Code: <a href="https://github.com/ChunyuanLI/RAS" rel="external noopener nofollow" class="link-external link-https">this https URL</a> </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item33'>[33]</a> <a href ="/abs/1901.00616" title="Abstract" id="1901.00616"> arXiv:1901.00616 </a> [<a href="/pdf/1901.00616" title="Download PDF" id="pdf-1901.00616" aria-labelledby="pdf-1901.00616">pdf</a>, <a href="/format/1901.00616" title="Other formats" id="oth-1901.00616" aria-labelledby="oth-1901.00616">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Volumetric Convolution: Automatic Representation Learning in Unit Ball </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ramasinghe,+S">Sameera Ramasinghe</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Khan,+S">Salman Khan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Barnes,+N">Nick Barnes</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item34'>[34]</a> <a href ="/abs/1901.00738" title="Abstract" id="1901.00738"> arXiv:1901.00738 </a> [<a href="/pdf/1901.00738" title="Download PDF" id="pdf-1901.00738" aria-labelledby="pdf-1901.00738">pdf</a>, <a href="/format/1901.00738" title="Other formats" id="oth-1901.00738" aria-labelledby="oth-1901.00738">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Resource-Scalable CNN Synthesis for IoT Applications </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Motamedi,+M">Mohammad Motamedi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Portillo,+F">Felix Portillo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Saffarpour,+M">Mahya Saffarpour</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fong,+D">Daniel Fong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ghiasi,+S">Soheil Ghiasi</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 7 Pages, 3 Figures, 4 Tables </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item35'>[35]</a> <a href ="/abs/1901.00770" title="Abstract" id="1901.00770"> arXiv:1901.00770 </a> [<a href="/pdf/1901.00770" title="Download PDF" id="pdf-1901.00770" aria-labelledby="pdf-1901.00770">pdf</a>, <a href="/format/1901.00770" title="Other formats" id="oth-1901.00770" aria-labelledby="oth-1901.00770">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Personalized explanation in machine learning: A conceptualization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Schneider,+J">Johanes Schneider</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Handali,+J">Joshua Handali</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted at 27th European Conference on Information Systems (ECIS 2019), Stockholm-Uppsala, Sweden, June 2019 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item36'>[36]</a> <a href ="/abs/1901.00785" title="Abstract" id="1901.00785"> arXiv:1901.00785 </a> [<a href="/pdf/1901.00785" title="Download PDF" id="pdf-1901.00785" aria-labelledby="pdf-1901.00785">pdf</a>, <a href="/format/1901.00785" title="Other formats" id="oth-1901.00785" aria-labelledby="oth-1901.00785">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A^2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Xu,+K">Kui Xu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Z">Zhe Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shi,+J">Jiangping Shi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+H">Hongsheng Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Q+C">Qiangfeng Cliff Zhang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 8 pages, 5 figures, 4 tables </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> published on AAAI2019 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Quantitative Methods (q-bio.QM); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item37'>[37]</a> <a href ="/abs/1901.00786" title="Abstract" id="1901.00786"> arXiv:1901.00786 </a> [<a href="/pdf/1901.00786" title="Download PDF" id="pdf-1901.00786" aria-labelledby="pdf-1901.00786">pdf</a>, <a href="/format/1901.00786" title="Other formats" id="oth-1901.00786" aria-labelledby="oth-1901.00786">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Towards Global Remote Discharge Estimation: Using the Few to Estimate The Many </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Gigi,+Y">Yotam Gigi</a> (1), <a href="https://arxiv.org/search/cs?searchtype=author&query=Elidan,+G">Gal Elidan</a> (1), <a href="https://arxiv.org/search/cs?searchtype=author&query=Hassidim,+A">Avinatan Hassidim</a> (2), <a href="https://arxiv.org/search/cs?searchtype=author&query=Matias,+Y">Yossi Matias</a> (3), <a href="https://arxiv.org/search/cs?searchtype=author&query=Moshe,+Z">Zach Moshe</a> (3), <a href="https://arxiv.org/search/cs?searchtype=author&query=Nevo,+S">Sella Nevo</a> (3), <a href="https://arxiv.org/search/cs?searchtype=author&query=Shalev,+G">Guy Shalev</a> (3), <a href="https://arxiv.org/search/cs?searchtype=author&query=Wiesel,+A">Ami Wiesel</a> (1) ((1) Google Research and The Hebrew University of Jerusalem Israel, (2) Google Research and Bar-Ilan University, (3) Google Research)</div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> The 4-page paper sent to NeurIPS 2018 AI for social good workshop </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item38'>[38]</a> <a href ="/abs/1901.00838" title="Abstract" id="1901.00838"> arXiv:1901.00838 </a> [<a href="/pdf/1901.00838" title="Download PDF" id="pdf-1901.00838" aria-labelledby="pdf-1901.00838">pdf</a>, <a href="/format/1901.00838" title="Other formats" id="oth-1901.00838" aria-labelledby="oth-1901.00838">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Mazumdar,+E+V">Eric V. Mazumdar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jordan,+M+I">Michael I. Jordan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sastry,+S+S">S. Shankar Sastry</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Optimization and Control (math.OC); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item39'>[39]</a> <a href ="/abs/1901.00858" title="Abstract" id="1901.00858"> arXiv:1901.00858 </a> [<a href="/pdf/1901.00858" title="Download PDF" id="pdf-1901.00858" aria-labelledby="pdf-1901.00858">pdf</a>, <a href="/format/1901.00858" title="Other formats" id="oth-1901.00858" aria-labelledby="oth-1901.00858">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> HG-Caffe: Mobile and Embedded Neural Network GPU (OpenCL) Inference Engine with FP16 Supporting </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ji,+Z">Zhuoran Ji</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item40'>[40]</a> <a href ="/abs/1901.00862" title="Abstract" id="1901.00862"> arXiv:1901.00862 </a> [<a href="/pdf/1901.00862" title="Download PDF" id="pdf-1901.00862" aria-labelledby="pdf-1901.00862">pdf</a>, <a href="/format/1901.00862" title="Other formats" id="oth-1901.00862" aria-labelledby="oth-1901.00862">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Xu,+D">Duo Xu</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Neural and Evolutionary Computing (cs.NE); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item41'>[41]</a> <a href ="/abs/1901.00877" title="Abstract" id="1901.00877"> arXiv:1901.00877 </a> [<a href="/pdf/1901.00877" title="Download PDF" id="pdf-1901.00877" aria-labelledby="pdf-1901.00877">pdf</a>, <a href="/format/1901.00877" title="Other formats" id="oth-1901.00877" aria-labelledby="oth-1901.00877">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Network-based Multimodal Data Fusion Approach for Characterizing Dynamic Multimodal Physiological Patterns </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Fan,+M">Miaolin Fan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chou,+C">Chun-An Chou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yen,+S">Sheng-Che Yen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lin,+Y">Yingzi Lin</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Quantitative Methods (q-bio.QM); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item42'>[42]</a> <a href ="/abs/1901.00884" title="Abstract" id="1901.00884"> arXiv:1901.00884 </a> [<a href="/pdf/1901.00884" title="Download PDF" id="pdf-1901.00884" aria-labelledby="pdf-1901.00884">pdf</a>, <a href="/format/1901.00884" title="Other formats" id="oth-1901.00884" aria-labelledby="oth-1901.00884">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Subspace Match Probably Does Not Accurately Assess the Similarity of Learned Representations </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Johnson,+J">Jeremiah Johnson</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Neural and Evolutionary Computing (cs.NE); Commutative Algebra (math.AC); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item43'>[43]</a> <a href ="/abs/1901.00898" title="Abstract" id="1901.00898"> arXiv:1901.00898 </a> [<a href="/pdf/1901.00898" title="Download PDF" id="pdf-1901.00898" aria-labelledby="pdf-1901.00898">pdf</a>, <a href="/format/1901.00898" title="Other formats" id="oth-1901.00898" aria-labelledby="oth-1901.00898">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Imminent Collision Mitigation with Reinforcement Learning and Vision </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Porav,+H">Horia Porav</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Newman,+P">Paul Newman</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Presented at ITSC2018 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item44'>[44]</a> <a href ="/abs/1901.00902" title="Abstract" id="1901.00902"> arXiv:1901.00902 </a> [<a href="/pdf/1901.00902" title="Download PDF" id="pdf-1901.00902" aria-labelledby="pdf-1901.00902">pdf</a>, <a href="/format/1901.00902" title="Other formats" id="oth-1901.00902" aria-labelledby="oth-1901.00902">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Model for Learned Bloom Filters, and Optimizing by Sandwiching </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Mitzenmacher,+M">Michael Mitzenmacher</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 12 pages; the complete version of the paper that appears in NIPS 2018, including addendum on learned Bloomier filters. arXiv admin note: substantial text overlap with <a href="https://arxiv.org/abs/1802.00884" data-arxiv-id="1802.00884" class="link-https">arXiv:1802.00884</a>, <a href="https://arxiv.org/abs/1803.01474" data-arxiv-id="1803.01474" class="link-https">arXiv:1803.01474</a> </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Databases (cs.DB); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item45'>[45]</a> <a href ="/abs/1901.00943" title="Abstract" id="1901.00943"> arXiv:1901.00943 </a> [<a href="/pdf/1901.00943" title="Download PDF" id="pdf-1901.00943" aria-labelledby="pdf-1901.00943">pdf</a>, <a href="/format/1901.00943" title="Other formats" id="oth-1901.00943" aria-labelledby="oth-1901.00943">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Self-supervised Learning of Image Embedding for Continuous Control </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Florensa,+C">Carlos Florensa</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Degrave,+J">Jonas Degrave</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Heess,+N">Nicolas Heess</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Springenberg,+J+T">Jost Tobias Springenberg</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Riedmiller,+M">Martin Riedmiller</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Contributed talk at Inference to Control workshop at NeurIPS2018 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Robotics (cs.RO) </div> </div> </dd> <dt> <a name='item46'>[46]</a> <a href ="/abs/1901.00952" title="Abstract" id="1901.00952"> arXiv:1901.00952 </a> [<a href="/pdf/1901.00952" title="Download PDF" id="pdf-1901.00952" aria-labelledby="pdf-1901.00952">pdf</a>, <a href="/format/1901.00952" title="Other formats" id="oth-1901.00952" aria-labelledby="oth-1901.00952">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Space Expansion of Feature Selection for Designing more Accurate Error Predictors </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Nikkhah,+S+T">Shayan Tabatabaei Nikkhah</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kamal,+M">Mehdi Kamal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Afzali-Kusha,+A">Ali Afzali-Kusha</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pedram,+M">Massoud Pedram</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item47'>[47]</a> <a href ="/abs/1901.00959" title="Abstract" id="1901.00959"> arXiv:1901.00959 </a> [<a href="/pdf/1901.00959" title="Download PDF" id="pdf-1901.00959" aria-labelledby="pdf-1901.00959">pdf</a>, <a href="/format/1901.00959" title="Other formats" id="oth-1901.00959" aria-labelledby="oth-1901.00959">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> QFlow: A Learning Approach to High QoE Video Streaming at the Wireless Edge </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Bhattacharyya,+R">Rajarshi Bhattacharyya</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bura,+A">Archana Bura</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rengarajan,+D">Desik Rengarajan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rumuly,+M">Mason Rumuly</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xia,+B">Bainan Xia</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shakkottai,+S">Srinivas Shakkottai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kalathil,+D">Dileep Kalathil</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mok,+R+K+P">Ricky K. P. Mok</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dhamdhere,+A">Amogh Dhamdhere</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Submitted to ToN in May, 2020 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Image and Video Processing (eess.IV); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item48'>[48]</a> <a href ="/abs/1901.00997" title="Abstract" id="1901.00997"> arXiv:1901.00997 </a> [<a href="/pdf/1901.00997" title="Download PDF" id="pdf-1901.00997" aria-labelledby="pdf-1901.00997">pdf</a>, <a href="/format/1901.00997" title="Other formats" id="oth-1901.00997" aria-labelledby="oth-1901.00997">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=A.,+P+L">Prashanth L. A.</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jagannathan,+K">Krishna Jagannathan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kolla,+R+K">Ravi Kumar Kolla</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item49'>[49]</a> <a href ="/abs/1901.01002" title="Abstract" id="1901.01002"> arXiv:1901.01002 </a> [<a href="/pdf/1901.01002" title="Download PDF" id="pdf-1901.01002" aria-labelledby="pdf-1901.01002">pdf</a>, <a href="/format/1901.01002" title="Other formats" id="oth-1901.01002" aria-labelledby="oth-1901.01002">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> On Reproducing Kernel Banach Spaces: Generic Definitions and Unified Framework of Constructions </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Lin,+R">Rongrong Lin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+H">Haizhang Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+J">Jun Zhang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Functional Analysis (math.FA); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item50'>[50]</a> <a href ="/abs/1901.01007" title="Abstract" id="1901.01007"> arXiv:1901.01007 </a> [<a href="/pdf/1901.01007" title="Download PDF" id="pdf-1901.01007" aria-labelledby="pdf-1901.01007">pdf</a>, <a href="/format/1901.01007" title="Other formats" id="oth-1901.01007" aria-labelledby="oth-1901.01007">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Geng,+T">Tong Geng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+T">Tianqi Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+A">Ang Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jin,+X">Xi Jin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Herbordt,+M">Martin Herbordt</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted by IEEE TRANSACTIONS ON COMPUTERS (TC) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (stat.ML) </div> </div> </dd> </dl> <div class='paging'>Total of 1100 entries : <span>1-50</span> <a href=/list/cs.LG/2019-01?skip=50&show=50>51-100</a> <a href=/list/cs.LG/2019-01?skip=100&show=50>101-150</a> <a href=/list/cs.LG/2019-01?skip=150&show=50>151-200</a> <span>...</span> <a href=/list/cs.LG/2019-01?skip=1050&show=50>1051-1100</a> </div> <div class='morefewer'>Showing up to 50 entries per page: <a href=/list/cs.LG/2019-01?skip=0&show=25 rel="nofollow"> fewer</a> | <a href=/list/cs.LG/2019-01?skip=0&show=100 rel="nofollow"> more</a> | <a href=/list/cs.LG/2019-01?skip=0&show=2000 rel="nofollow"> all</a> </div> </div> </div> </div> </main> <footer style="clear: both;"> <div class="columns is-desktop" role="navigation" aria-label="Secondary" style="margin: -0.75em -0.75em 0.75em -0.75em">  <div class="column" style="padding: 0;"> <div class="columns"> <div class="column"> <ul style="list-style: none; 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Machine Learning Jan 2019