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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Tumer, E"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" 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is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.12169">arXiv:2103.12169</a> <span> [<a href="https://arxiv.org/pdf/2103.12169">pdf</a>, <a href="https://arxiv.org/format/2103.12169">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1002/MRC.5212">10.1002/MRC.5212 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Pilot Study For Fragment Identification Using 2D NMR and Deep Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kuhn%2C+S">Stefan Kuhn</a>, <a href="/search/cs?searchtype=author&query=Tumer%2C+E">Eda Tumer</a>, <a href="/search/cs?searchtype=author&query=Colreavy-Donnelly%2C+S">Simon Colreavy-Donnelly</a>, <a href="/search/cs?searchtype=author&query=Borges%2C+R+M">Ricardo Moreira Borges</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2103.12169v1-abstract-short" style="display: inline;"> This paper presents a method to identify substructures in NMR spectra of mixtures, specifically 2D spectra, using a bespoke image-based Convolutional Neural Network application. This is done using HSQC and HMBC spectra separately and in combination. The application can reliably detect substructures in pure compounds, using a simple network. It can work for mixtures when trained on pure compounds o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.12169v1-abstract-full').style.display = 'inline'; document.getElementById('2103.12169v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.12169v1-abstract-full" style="display: none;"> This paper presents a method to identify substructures in NMR spectra of mixtures, specifically 2D spectra, using a bespoke image-based Convolutional Neural Network application. This is done using HSQC and HMBC spectra separately and in combination. The application can reliably detect substructures in pure compounds, using a simple network. It can work for mixtures when trained on pure compounds only. HMBC data and the combination of HMBC and HSQC show better results than HSQC alone. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.12169v1-abstract-full').style.display = 'none'; document.getElementById('2103.12169v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 3 figures, 3 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Magn Reson Chem 2021, 1 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1905.00976">arXiv:1905.00976</a> <span> [<a href="https://arxiv.org/pdf/1905.00976">pdf</a>, <a href="https://arxiv.org/format/1905.00976">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Collaborative Evolutionary Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Khadka%2C+S">Shauharda Khadka</a>, <a href="/search/cs?searchtype=author&query=Majumdar%2C+S">Somdeb Majumdar</a>, <a href="/search/cs?searchtype=author&query=Nassar%2C+T">Tarek Nassar</a>, <a href="/search/cs?searchtype=author&query=Dwiel%2C+Z">Zach Dwiel</a>, <a href="/search/cs?searchtype=author&query=Tumer%2C+E">Evren Tumer</a>, <a href="/search/cs?searchtype=author&query=Miret%2C+S">Santiago Miret</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Y">Yinyin Liu</a>, <a href="/search/cs?searchtype=author&query=Tumer%2C+K">Kagan Tumer</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1905.00976v2-abstract-short" style="display: inline;"> Deep reinforcement learning algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically struggle with achieving effective exploration and are extremely sensitive to the choice of hyperparameters. One reason is that most approaches use a noisy version of their operating policy to explore - thereby limiting the range of exploration. In this pap… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.00976v2-abstract-full').style.display = 'inline'; document.getElementById('1905.00976v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1905.00976v2-abstract-full" style="display: none;"> Deep reinforcement learning algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically struggle with achieving effective exploration and are extremely sensitive to the choice of hyperparameters. One reason is that most approaches use a noisy version of their operating policy to explore - thereby limiting the range of exploration. In this paper, we introduce Collaborative Evolutionary Reinforcement Learning (CERL), a scalable framework that comprises a portfolio of policies that simultaneously explore and exploit diverse regions of the solution space. A collection of learners - typically proven algorithms like TD3 - optimize over varying time-horizons leading to this diverse portfolio. All learners contribute to and use a shared replay buffer to achieve greater sample efficiency. Computational resources are dynamically distributed to favor the best learners as a form of online algorithm selection. Neuroevolution binds this entire process to generate a single emergent learner that exceeds the capabilities of any individual learner. Experiments in a range of continuous control benchmarks demonstrate that the emergent learner significantly outperforms its composite learners while remaining overall more sample-efficient - notably solving the Mujoco Humanoid benchmark where all of its composite learners (TD3) fail entirely in isolation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.00976v2-abstract-full').style.display = 'none'; document.getElementById('1905.00976v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 May, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 May, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Added link to public Github repo. Minor editorial changes. Order of authors modified to reflect ICML submission</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, PMLR 97, 2019 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1902.02441">arXiv:1902.02441</a> <span> [<a href="https://arxiv.org/pdf/1902.02441">pdf</a>, <a href="https://arxiv.org/format/1902.02441">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Artificial Intelligence for Prosthetics - challenge solutions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kidzi%C5%84ski%2C+%C5%81">艁ukasz Kidzi艅ski</a>, <a href="/search/cs?searchtype=author&query=Ong%2C+C">Carmichael Ong</a>, <a href="/search/cs?searchtype=author&query=Mohanty%2C+S+P">Sharada Prasanna Mohanty</a>, <a href="/search/cs?searchtype=author&query=Hicks%2C+J">Jennifer Hicks</a>, <a href="/search/cs?searchtype=author&query=Carroll%2C+S+F">Sean F. Carroll</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+B">Bo Zhou</a>, <a href="/search/cs?searchtype=author&query=Zeng%2C+H">Hongsheng Zeng</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+F">Fan Wang</a>, <a href="/search/cs?searchtype=author&query=Lian%2C+R">Rongzhong Lian</a>, <a href="/search/cs?searchtype=author&query=Tian%2C+H">Hao Tian</a>, <a href="/search/cs?searchtype=author&query=Ja%C5%9Bkowski%2C+W">Wojciech Ja艣kowski</a>, <a href="/search/cs?searchtype=author&query=Andersen%2C+G">Garrett Andersen</a>, <a href="/search/cs?searchtype=author&query=Lykkeb%C3%B8%2C+O+R">Odd Rune Lykkeb酶</a>, <a href="/search/cs?searchtype=author&query=Toklu%2C+N+E">Nihat Engin Toklu</a>, <a href="/search/cs?searchtype=author&query=Shyam%2C+P">Pranav Shyam</a>, <a href="/search/cs?searchtype=author&query=Srivastava%2C+R+K">Rupesh Kumar Srivastava</a>, <a href="/search/cs?searchtype=author&query=Kolesnikov%2C+S">Sergey Kolesnikov</a>, <a href="/search/cs?searchtype=author&query=Hrinchuk%2C+O">Oleksii Hrinchuk</a>, <a href="/search/cs?searchtype=author&query=Pechenko%2C+A">Anton Pechenko</a>, <a href="/search/cs?searchtype=author&query=Ljungstr%C3%B6m%2C+M">Mattias Ljungstr枚m</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Z">Zhen Wang</a>, <a href="/search/cs?searchtype=author&query=Hu%2C+X">Xu Hu</a>, <a href="/search/cs?searchtype=author&query=Hu%2C+Z">Zehong Hu</a>, <a href="/search/cs?searchtype=author&query=Qiu%2C+M">Minghui Qiu</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+J">Jun Huang</a> , et al. (25 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1902.02441v1-abstract-short" style="display: inline;"> In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector. Top participants were invited to describe their algorithms. In this work, we describe the challenge and present thirteen solutions that used deep reinforcement learning approaches. Many s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.02441v1-abstract-full').style.display = 'inline'; document.getElementById('1902.02441v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1902.02441v1-abstract-full" style="display: none;"> In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector. Top participants were invited to describe their algorithms. In this work, we describe the challenge and present thirteen solutions that used deep reinforcement learning approaches. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each team implemented different modifications of the known algorithms by, for example, dividing the task into subtasks, learning low-level control, or by incorporating expert knowledge and using imitation learning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.02441v1-abstract-full').style.display = 'none'; document.getElementById('1902.02441v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 February, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1812.05212">arXiv:1812.05212</a> <span> [<a href="https://arxiv.org/pdf/1812.05212">pdf</a>, <a href="https://arxiv.org/ps/1812.05212">ps</a>, <a href="https://arxiv.org/format/1812.05212">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Conditional Graph Neural Processes: A Functional Autoencoder Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nassar%2C+M">Marcel Nassar</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+X">Xin Wang</a>, <a href="/search/cs?searchtype=author&query=Tumer%2C+E">Evren Tumer</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1812.05212v1-abstract-short" style="display: inline;"> We introduce a novel encoder-decoder architecture to embed functional processes into latent vector spaces. This embedding can then be decoded to sample the encoded functions over any arbitrary domain. This autoencoder generalizes the recently introduced Conditional Neural Process (CNP) model of random processes. Our architecture employs the latest advances in graph neural networks to process irreg… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1812.05212v1-abstract-full').style.display = 'inline'; document.getElementById('1812.05212v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1812.05212v1-abstract-full" style="display: none;"> We introduce a novel encoder-decoder architecture to embed functional processes into latent vector spaces. This embedding can then be decoded to sample the encoded functions over any arbitrary domain. This autoencoder generalizes the recently introduced Conditional Neural Process (CNP) model of random processes. Our architecture employs the latest advances in graph neural networks to process irregularly sampled functions. Thus, we refer to our model as Conditional Graph Neural Process (CGNP). Graph neural networks can effectively exploit `local' structures of the metric spaces over which the functions/processes are defined. The contributions of this paper are twofold: (i) a novel graph-based encoder-decoder architecture for functional and process embeddings, and (ii) a demonstration of the importance of using the structure of metric spaces for this type of representations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1812.05212v1-abstract-full').style.display = 'none'; document.getElementById('1812.05212v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 December, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">3 pages, 1 figure, 1 table, published in the Third Workshop on Bayesian Deep Learning (NeurIPS 2018), Montr茅al, Canada</span> </p> 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