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Machine Learning Oct 2024
<!DOCTYPE html> <html lang="en"> <head> <title>Machine Learning Oct 2024</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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class='morefewer'>Showing up to 50 entries per page: <a href=/list/cs.LG/2024-10?skip=0&show=25 rel="nofollow"> fewer</a> | <a href=/list/cs.LG/2024-10?skip=0&show=100 rel="nofollow"> more</a> | <a href=/list/cs.LG/2024-10?skip=0&show=2000 rel="nofollow"> all</a> </div> <dl id='articles'> <dt> <a name='item1'>[1]</a> <a href ="/abs/2410.00022" title="Abstract" id="2410.00022"> arXiv:2410.00022 </a> [<a href="/pdf/2410.00022" title="Download PDF" id="pdf-2410.00022" aria-labelledby="pdf-2410.00022">pdf</a>, <a href="https://arxiv.org/html/2410.00022v1" title="View HTML" id="html-2410.00022" aria-labelledby="html-2410.00022" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00022" title="Other formats" id="oth-2410.00022" aria-labelledby="oth-2410.00022">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> TREB: a BERT attempt for imputing tabular data imputation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+S">Shuyue Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+W">Wenjun Zhou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jiang,+H+d">Han drk-m-s Jiang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+S">Shuo Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zheng,+R">Ren Zheng</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 12 pages, 7 figures </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='item2'>[2]</a> <a href ="/abs/2410.00034" title="Abstract" id="2410.00034"> arXiv:2410.00034 </a> [<a href="/pdf/2410.00034" title="Download PDF" id="pdf-2410.00034" aria-labelledby="pdf-2410.00034">pdf</a>, <a href="https://arxiv.org/html/2410.00034v1" title="View HTML" id="html-2410.00034" aria-labelledby="html-2410.00034" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00034" title="Other formats" id="oth-2410.00034" aria-labelledby="oth-2410.00034">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Prediction and Detection of Terminal Diseases Using Internet of Medical Things: A Review </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Otapo,+A+T">Akeem Temitope Otapo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Othmani,+A">Alice Othmani</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Khodabandelou,+G">Ghazaleh Khodabandelou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ming,+Z">Zuheng Ming</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='item3'>[3]</a> <a href ="/abs/2410.00038" title="Abstract" id="2410.00038"> arXiv:2410.00038 </a> [<a href="/pdf/2410.00038" title="Download PDF" id="pdf-2410.00038" aria-labelledby="pdf-2410.00038">pdf</a>, <a href="https://arxiv.org/html/2410.00038v1" title="View HTML" id="html-2410.00038" aria-labelledby="html-2410.00038" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00038" title="Other formats" id="oth-2410.00038" aria-labelledby="oth-2410.00038">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Novel Spinor-Based Embedding Model for Transformers </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=White,+R">Rick White</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 22 pages, 8 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computation and Language (cs.CL) </div> </div> </dd> <dt> <a name='item4'>[4]</a> <a href ="/abs/2410.00049" title="Abstract" id="2410.00049"> arXiv:2410.00049 </a> [<a href="/pdf/2410.00049" title="Download PDF" id="pdf-2410.00049" aria-labelledby="pdf-2410.00049">pdf</a>, <a href="https://arxiv.org/html/2410.00049v2" title="View HTML" id="html-2410.00049" aria-labelledby="html-2410.00049" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00049" title="Other formats" id="oth-2410.00049" aria-labelledby="oth-2410.00049">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wan,+G">Guancheng Wan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+Z">Zewen Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lau,+M+S">Max S.Y. Lau</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Prakash,+B+A">B. Aditya Prakash</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jin,+W">Wei Jin</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI) </div> </div> </dd> <dt> <a name='item5'>[5]</a> <a href ="/abs/2410.00050" title="Abstract" id="2410.00050"> arXiv:2410.00050 </a> [<a href="/pdf/2410.00050" title="Download PDF" id="pdf-2410.00050" aria-labelledby="pdf-2410.00050">pdf</a>, <a href="https://arxiv.org/html/2410.00050v1" title="View HTML" id="html-2410.00050" aria-labelledby="html-2410.00050" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00050" title="Other formats" id="oth-2410.00050" aria-labelledby="oth-2410.00050">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> CycleBNN: Cyclic Precision Training in Binary Neural Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Fontana,+F">Federico Fontana</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lanzino,+R">Romeo Lanzino</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Diko,+A">Anxhelo Diko</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Foresti,+G+L">Gian Luca Foresti</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cinque,+L">Luigi Cinque</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Published at Workshop CADL, ECCV-2024 </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) </div> </div> </dd> <dt> <a name='item6'>[6]</a> <a href ="/abs/2410.00051" title="Abstract" id="2410.00051"> arXiv:2410.00051 </a> [<a href="/pdf/2410.00051" title="Download PDF" id="pdf-2410.00051" aria-labelledby="pdf-2410.00051">pdf</a>, <a href="https://arxiv.org/html/2410.00051v2" title="View HTML" id="html-2410.00051" aria-labelledby="html-2410.00051" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00051" title="Other formats" id="oth-2410.00051" aria-labelledby="oth-2410.00051">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+H">Haoran Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jiang,+Z">Zhennan Jiang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+Y">Yuhui Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhao,+D">Dongbin Zhao</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted at the Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS2024) </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) </div> </div> </dd> <dt> <a name='item7'>[7]</a> <a href ="/abs/2410.00052" title="Abstract" id="2410.00052"> arXiv:2410.00052 </a> [<a href="/pdf/2410.00052" title="Download PDF" id="pdf-2410.00052" aria-labelledby="pdf-2410.00052">pdf</a>, <a href="/format/2410.00052" title="Other formats" id="oth-2410.00052" aria-labelledby="oth-2410.00052">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> DelayPTC-LLM: Metro Passenger Travel Choice Prediction under Train Delays with Large Language Models </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+C">Chen Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=He,+Y">Yuxin He</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+H">Hao Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+J">Jingjing Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Luo,+Q">Qin Luo</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 15 pages,4 figures </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='item8'>[8]</a> <a href ="/abs/2410.00053" title="Abstract" id="2410.00053"> arXiv:2410.00053 </a> [<a href="/pdf/2410.00053" title="Download PDF" id="pdf-2410.00053" aria-labelledby="pdf-2410.00053">pdf</a>, <a href="https://arxiv.org/html/2410.00053v1" title="View HTML" id="html-2410.00053" aria-labelledby="html-2410.00053" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00053" title="Other formats" id="oth-2410.00053" aria-labelledby="oth-2410.00053">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Frequency-adaptive Multi-scale Deep Neural Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+J">Jizu Huang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=You,+R">Rukang You</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+T">Tao Zhou</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='item9'>[9]</a> <a href ="/abs/2410.00054" title="Abstract" id="2410.00054"> arXiv:2410.00054 </a> [<a href="/pdf/2410.00054" title="Download PDF" id="pdf-2410.00054" aria-labelledby="pdf-2410.00054">pdf</a>, <a href="https://arxiv.org/html/2410.00054v2" title="View HTML" id="html-2410.00054" aria-labelledby="html-2410.00054" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00054" title="Other formats" id="oth-2410.00054" aria-labelledby="oth-2410.00054">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Transferable Unsupervised Outlier Detection Framework for Human Semantic Trajectories </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Z">Zheng Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Amiri,+H">Hossein Amiri</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yu,+D">Dazhou Yu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hu,+Y">Yuntong Hu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhao,+L">Liang Zhao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zufle,+A">Andreas Zufle</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> This is an accepted paper on <a href="https://sigspatial2024.sigspatial.org/accepted-papers/" 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> </div> </div> </dd> <dt> <a name='item10'>[10]</a> <a href ="/abs/2410.00057" title="Abstract" id="2410.00057"> arXiv:2410.00057 </a> [<a href="/pdf/2410.00057" title="Download PDF" id="pdf-2410.00057" aria-labelledby="pdf-2410.00057">pdf</a>, <a href="https://arxiv.org/html/2410.00057v1" title="View HTML" id="html-2410.00057" aria-labelledby="html-2410.00057" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00057" title="Other formats" id="oth-2410.00057" aria-labelledby="oth-2410.00057">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> STTM: A New Approach Based Spatial-Temporal Transformer And Memory Network For Real-time Pressure Signal In On-demand Food Delivery </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+J">Jiang Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wei,+H">Haibin Wei</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xu,+X">Xiaowei Xu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shi,+J">Jiacheng Shi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nie,+J">Jian Nie</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Du,+L">Longzhi Du</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jiang,+T">Taixu Jiang</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='item11'>[11]</a> <a href ="/abs/2410.00061" title="Abstract" id="2410.00061"> arXiv:2410.00061 </a> [<a href="/pdf/2410.00061" title="Download PDF" id="pdf-2410.00061" aria-labelledby="pdf-2410.00061">pdf</a>, <a href="https://arxiv.org/html/2410.00061v1" title="View HTML" id="html-2410.00061" aria-labelledby="html-2410.00061" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00061" title="Other formats" id="oth-2410.00061" aria-labelledby="oth-2410.00061">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Neural Decompiling of Tracr Transformers </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Thurnherr,+H">Hannes Thurnherr</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Riesen,+K">Kaspar Riesen</a></div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Artificial Neural Networks in Pattern Recognition, Lecture Notes in Computer Science, vol. 14252, Springer, 2024, pp. 25-36 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item12'>[12]</a> <a href ="/abs/2410.00064" title="Abstract" id="2410.00064"> arXiv:2410.00064 </a> [<a href="/pdf/2410.00064" title="Download PDF" id="pdf-2410.00064" aria-labelledby="pdf-2410.00064">pdf</a>, <a href="https://arxiv.org/html/2410.00064v2" title="View HTML" id="html-2410.00064" aria-labelledby="html-2410.00064" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00064" title="Other formats" id="oth-2410.00064" aria-labelledby="oth-2410.00064">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> M2Distill: Multi-Modal Distillation for Lifelong Imitation Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Roy,+K">Kaushik Roy</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dissanayake,+A">Akila Dissanayake</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tidd,+B">Brendan Tidd</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Moghadam,+P">Peyman Moghadam</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Submitted to ICRA2025 </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); Robotics (cs.RO) </div> </div> </dd> <dt> <a name='item13'>[13]</a> <a href ="/abs/2410.00074" title="Abstract" id="2410.00074"> arXiv:2410.00074 </a> [<a href="/pdf/2410.00074" title="Download PDF" id="pdf-2410.00074" aria-labelledby="pdf-2410.00074">pdf</a>, <a href="https://arxiv.org/html/2410.00074v1" title="View HTML" id="html-2410.00074" aria-labelledby="html-2410.00074" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00074" title="Other formats" id="oth-2410.00074" aria-labelledby="oth-2410.00074">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Collaborative Knowledge Distillation via a Learning-by-Education Node Community </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kaimakamidis,+A">Anestis Kaimakamidis</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mademlis,+I">Ioannis Mademlis</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pitas,+I">Ioannis Pitas</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='item14'>[14]</a> <a href ="/abs/2410.00083" title="Abstract" id="2410.00083"> arXiv:2410.00083 </a> [<a href="/pdf/2410.00083" title="Download PDF" id="pdf-2410.00083" aria-labelledby="pdf-2410.00083">pdf</a>, <a href="https://arxiv.org/html/2410.00083v1" title="View HTML" id="html-2410.00083" aria-labelledby="html-2410.00083" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00083" title="Other formats" id="oth-2410.00083" aria-labelledby="oth-2410.00083">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Survey on Diffusion Models for Inverse Problems </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Daras,+G">Giannis Daras</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chung,+H">Hyungjin Chung</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lai,+C">Chieh-Hsin Lai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mitsufuji,+Y">Yuki Mitsufuji</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ye,+J+C">Jong Chul Ye</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Milanfar,+P">Peyman Milanfar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dimakis,+A+G">Alexandros G. Dimakis</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Delbracio,+M">Mauricio Delbracio</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Work in progress. 38 pages </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) </div> </div> </dd> <dt> <a name='item15'>[15]</a> <a href ="/abs/2410.00085" title="Abstract" id="2410.00085"> arXiv:2410.00085 </a> [<a href="/pdf/2410.00085" title="Download PDF" id="pdf-2410.00085" aria-labelledby="pdf-2410.00085">pdf</a>, <a href="https://arxiv.org/html/2410.00085v1" title="View HTML" id="html-2410.00085" aria-labelledby="html-2410.00085" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00085" title="Other formats" id="oth-2410.00085" aria-labelledby="oth-2410.00085">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Fine-tuning Vision Classifiers On A Budget </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kumar,+S">Sunil Kumar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sandler,+T">Ted Sandler</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Varshavskaya,+P">Paulina Varshavskaya</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 8 pages, 5 figures </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) </div> </div> </dd> <dt> <a name='item16'>[16]</a> <a href ="/abs/2410.00121" title="Abstract" id="2410.00121"> arXiv:2410.00121 </a> [<a href="/pdf/2410.00121" title="Download PDF" id="pdf-2410.00121" aria-labelledby="pdf-2410.00121">pdf</a>, <a href="/format/2410.00121" title="Other formats" id="oth-2410.00121" aria-labelledby="oth-2410.00121">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Using fractal dimension to predict the risk of intra cranial aneurysm rupture with machine learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Elavarthi,+P">Pradyumna Elavarthi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ralescu,+A">Anca Ralescu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Johnson,+M+D">Mark D. Johnson</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Prestigiacomo,+C+J">Charles J. Prestigiacomo</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='item17'>[17]</a> <a href ="/abs/2410.00131" title="Abstract" id="2410.00131"> arXiv:2410.00131 </a> [<a href="/pdf/2410.00131" title="Download PDF" id="pdf-2410.00131" aria-labelledby="pdf-2410.00131">pdf</a>, <a href="https://arxiv.org/html/2410.00131v2" title="View HTML" id="html-2410.00131" aria-labelledby="html-2410.00131" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00131" title="Other formats" id="oth-2410.00131" aria-labelledby="oth-2410.00131">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+J">Ji Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ren,+J">Jiaxiang Ren</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jin,+R">Ruoming Jin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Z">Zijie Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+Y">Yang Zhou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Valduriez,+P">Patrick Valduriez</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dou,+D">Dejing Dou</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 27 pages, 8 figures, 14 tables, to appear in EMNLP 2024 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC) </div> </div> </dd> <dt> <a name='item18'>[18]</a> <a href ="/abs/2410.00169" title="Abstract" id="2410.00169"> arXiv:2410.00169 </a> [<a href="/pdf/2410.00169" title="Download PDF" id="pdf-2410.00169" aria-labelledby="pdf-2410.00169">pdf</a>, <a href="https://arxiv.org/html/2410.00169v1" title="View HTML" id="html-2410.00169" aria-labelledby="html-2410.00169" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00169" title="Other formats" id="oth-2410.00169" aria-labelledby="oth-2410.00169">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> (Almost) Smooth Sailing: Towards Numerical Stability of Neural Networks Through Differentiable Regularization of the Condition Number </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Nenov,+R">Rossen Nenov</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Haider,+D">Daniel Haider</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Balazs,+P">Peter Balazs</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted at ICML24 Workshop: Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators </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='item19'>[19]</a> <a href ="/abs/2410.00171" title="Abstract" id="2410.00171"> arXiv:2410.00171 </a> [<a href="/pdf/2410.00171" title="Download PDF" id="pdf-2410.00171" aria-labelledby="pdf-2410.00171">pdf</a>, <a href="https://arxiv.org/html/2410.00171v2" title="View HTML" id="html-2410.00171" aria-labelledby="html-2410.00171" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00171" title="Other formats" id="oth-2410.00171" aria-labelledby="oth-2410.00171">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Basis-to-Basis Operator Learning Using Function Encoders </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ingebrand,+T">Tyler Ingebrand</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Thorpe,+A+J">Adam J. Thorpe</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Goswami,+S">Somdatta Goswami</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kumar,+K">Krishna Kumar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Topcu,+U">Ufuk Topcu</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='item20'>[20]</a> <a href ="/abs/2410.00173" title="Abstract" id="2410.00173"> arXiv:2410.00173 </a> [<a href="/pdf/2410.00173" title="Download PDF" id="pdf-2410.00173" aria-labelledby="pdf-2410.00173">pdf</a>, <a href="https://arxiv.org/html/2410.00173v1" title="View HTML" id="html-2410.00173" aria-labelledby="html-2410.00173" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00173" title="Other formats" id="oth-2410.00173" aria-labelledby="oth-2410.00173">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> GaNDLF-Synth: A Framework to Democratize Generative AI for (Bio)Medical Imaging </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Pati,+S">Sarthak Pati</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mazurek,+S">Szymon Mazurek</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bakas,+S">Spyridon Bakas</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='item21'>[21]</a> <a href ="/abs/2410.00215" title="Abstract" id="2410.00215"> arXiv:2410.00215 </a> [<a href="/pdf/2410.00215" title="Download PDF" id="pdf-2410.00215" aria-labelledby="pdf-2410.00215">pdf</a>, <a href="https://arxiv.org/html/2410.00215v1" title="View HTML" id="html-2410.00215" aria-labelledby="html-2410.00215" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00215" title="Other formats" id="oth-2410.00215" aria-labelledby="oth-2410.00215">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Characterizing and Efficiently Accelerating Multimodal Generation Model Inference </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+Y">Yejin Lee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+A">Anna Sun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hosmer,+B">Basil Hosmer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Acun,+B">Bilge Acun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Balioglu,+C">Can Balioglu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+C">Changhan Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hernandez,+C+D">Charles David Hernandez</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Puhrsch,+C">Christian Puhrsch</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Haziza,+D">Daniel Haziza</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Guessous,+D">Driss Guessous</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Massa,+F">Francisco Massa</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kahn,+J">Jacob Kahn</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wan,+J">Jeffrey Wan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Reizenstein,+J">Jeremy Reizenstein</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhai,+J">Jiaqi Zhai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Isaacson,+J">Joe Isaacson</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=Pino,+J">Juan Pino</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sadagopan,+K+R">Kaushik Ram Sadagopan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shamis,+L">Leonid Shamis</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ma,+L">Linjian Ma</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hwang,+M">Min-Jae Hwang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+M">Mingda Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Elhoushi,+M">Mostafa Elhoushi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rodriguez,+P">Pedro Rodriguez</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pasunuru,+R">Ram Pasunuru</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yih,+S">Scott Yih</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Popuri,+S">Sravya Popuri</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+X">Xing Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wu,+C">Carole-Jean Wu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 13 pages including references. 8 Figures. Under review to HPCA 2025 Industry Track </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='item22'>[22]</a> <a href ="/abs/2410.00225" title="Abstract" id="2410.00225"> arXiv:2410.00225 </a> [<a href="/pdf/2410.00225" title="Download PDF" id="pdf-2410.00225" aria-labelledby="pdf-2410.00225">pdf</a>, <a href="https://arxiv.org/html/2410.00225v1" title="View HTML" id="html-2410.00225" aria-labelledby="html-2410.00225" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00225" title="Other formats" id="oth-2410.00225" aria-labelledby="oth-2410.00225">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Probabilistic Classification of Near-Surface Shallow-Water Sediments using A Portable Free-Fall Penetrometer </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Rahman,+M+R">Md Rejwanur Rahman</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rodriguez-Marek,+A">Adrian Rodriguez-Marek</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Stark,+N">Nina Stark</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Massey,+G">Grace Massey</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Friedrichs,+C">Carl Friedrichs</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dorgan,+K+M">Kelly M. Dorgan</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Applications (stat.AP) </div> </div> </dd> <dt> <a name='item23'>[23]</a> <a href ="/abs/2410.00232" title="Abstract" id="2410.00232"> arXiv:2410.00232 </a> [<a href="/pdf/2410.00232" title="Download PDF" id="pdf-2410.00232" aria-labelledby="pdf-2410.00232">pdf</a>, <a href="https://arxiv.org/html/2410.00232v1" title="View HTML" id="html-2410.00232" aria-labelledby="html-2410.00232" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00232" title="Other formats" id="oth-2410.00232" aria-labelledby="oth-2410.00232">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Preconditioning for Accelerated Gradient Descent Optimization and Regularization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ye,+Q">Qiang Ye</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 7 pages </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Numerical Analysis (math.NA); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item24'>[24]</a> <a href ="/abs/2410.00242" title="Abstract" id="2410.00242"> arXiv:2410.00242 </a> [<a href="/pdf/2410.00242" title="Download PDF" id="pdf-2410.00242" aria-labelledby="pdf-2410.00242">pdf</a>, <a href="https://arxiv.org/html/2410.00242v1" title="View HTML" id="html-2410.00242" aria-labelledby="html-2410.00242" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00242" title="Other formats" id="oth-2410.00242" aria-labelledby="oth-2410.00242">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Quantized and Asynchronous Federated Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ortega,+T">Tomas Ortega</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jafarkhani,+H">Hamid Jafarkhani</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Signal Processing (eess.SP); Optimization and Control (math.OC) </div> </div> </dd> <dt> <a name='item25'>[25]</a> <a href ="/abs/2410.00256" title="Abstract" id="2410.00256"> arXiv:2410.00256 </a> [<a href="/pdf/2410.00256" title="Download PDF" id="pdf-2410.00256" aria-labelledby="pdf-2410.00256">pdf</a>, <a href="https://arxiv.org/html/2410.00256v2" title="View HTML" id="html-2410.00256" aria-labelledby="html-2410.00256" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00256" title="Other formats" id="oth-2410.00256" aria-labelledby="oth-2410.00256">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Enhanced Credit Score Prediction Using Ensemble Deep Learning Model </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Xing,+Q">Qianwen Xing</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yu,+C">Chang Yu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+S">Sining Huang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zheng,+Q">Qi Zheng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mu,+X">Xingyu Mu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+M">Mengying Sun</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> This paper have been accepted by sci of AI Journal </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='item26'>[26]</a> <a href ="/abs/2410.00273" title="Abstract" id="2410.00273"> arXiv:2410.00273 </a> [<a href="/pdf/2410.00273" title="Download PDF" id="pdf-2410.00273" aria-labelledby="pdf-2410.00273">pdf</a>, <a href="https://arxiv.org/html/2410.00273v1" title="View HTML" id="html-2410.00273" aria-labelledby="html-2410.00273" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00273" title="Other formats" id="oth-2410.00273" aria-labelledby="oth-2410.00273">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Comprehensive Performance Modeling and System Design Insights for Foundation Models </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Subramanian,+S">Shashank Subramanian</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rrapaj,+E">Ermal Rrapaj</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Harrington,+P">Peter Harrington</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chheda,+S">Smeet Chheda</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Farrell,+S">Steven Farrell</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Austin,+B">Brian Austin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Williams,+S">Samuel Williams</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wright,+N">Nicholas Wright</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bhimji,+W">Wahid Bhimji</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 17 pages, PMBS 2024 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Distributed, Parallel, and Cluster Computing (cs.DC) </div> </div> </dd> <dt> <a name='item27'>[27]</a> <a href ="/abs/2410.00296" title="Abstract" id="2410.00296"> arXiv:2410.00296 </a> [<a href="/pdf/2410.00296" title="Download PDF" id="pdf-2410.00296" aria-labelledby="pdf-2410.00296">pdf</a>, <a href="https://arxiv.org/html/2410.00296v1" title="View HTML" id="html-2410.00296" aria-labelledby="html-2410.00296" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00296" title="Other formats" id="oth-2410.00296" aria-labelledby="oth-2410.00296">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> VLMGuard: Defending VLMs against Malicious Prompts via Unlabeled Data </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Du,+X">Xuefeng Du</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ghosh,+R">Reshmi Ghosh</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sim,+R">Robert Sim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Salem,+A">Ahmed Salem</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Carvalho,+V">Vitor Carvalho</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lawton,+E">Emily Lawton</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+Y">Yixuan Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Stokes,+J+W">Jack W. Stokes</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> arXiv admin note: text overlap with <a href="https://arxiv.org/abs/2409.17504" data-arxiv-id="2409.17504" class="link-https">arXiv:2409.17504</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) </div> </div> </dd> <dt> <a name='item28'>[28]</a> <a href ="/abs/2410.00327" title="Abstract" id="2410.00327"> arXiv:2410.00327 </a> [<a href="/pdf/2410.00327" title="Download PDF" id="pdf-2410.00327" aria-labelledby="pdf-2410.00327">pdf</a>, <a href="https://arxiv.org/html/2410.00327v1" title="View HTML" id="html-2410.00327" aria-labelledby="html-2410.00327" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00327" title="Other formats" id="oth-2410.00327" aria-labelledby="oth-2410.00327">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Hua,+C">Chenqing Hua</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+Y">Yong Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+D">Dinghuai Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+O">Odin Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Luan,+S">Sitao Luan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+K+K">Kevin K. Yang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wolf,+G">Guy Wolf</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Precup,+D">Doina Precup</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zheng,+S">Shuangjia Zheng</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Quantitative Methods (q-bio.QM) </div> </div> </dd> <dt> <a name='item29'>[29]</a> <a href ="/abs/2410.00340" title="Abstract" id="2410.00340"> arXiv:2410.00340 </a> [<a href="/pdf/2410.00340" title="Download PDF" id="pdf-2410.00340" aria-labelledby="pdf-2410.00340">pdf</a>, <a href="https://arxiv.org/html/2410.00340v3" title="View HTML" id="html-2410.00340" aria-labelledby="html-2410.00340" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00340" title="Other formats" id="oth-2410.00340" aria-labelledby="oth-2410.00340">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Sparse Attention Decomposition Applied to Circuit Tracing </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Franco,+G">Gabriel Franco</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Crovella,+M">Mark Crovella</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Computation and Language (cs.CL) </div> </div> </dd> <dt> <a name='item30'>[30]</a> <a href ="/abs/2410.00345" title="Abstract" id="2410.00345"> arXiv:2410.00345 </a> [<a href="/pdf/2410.00345" title="Download PDF" id="pdf-2410.00345" aria-labelledby="pdf-2410.00345">pdf</a>, <a href="/format/2410.00345" title="Other formats" id="oth-2410.00345" aria-labelledby="oth-2410.00345">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Taxonomy of Loss Functions for Stochastic Optimal Control </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Domingo-Enrich,+C">Carles Domingo-Enrich</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='item31'>[31]</a> <a href ="/abs/2410.00357" title="Abstract" id="2410.00357"> arXiv:2410.00357 </a> [<a href="/pdf/2410.00357" title="Download PDF" id="pdf-2410.00357" aria-labelledby="pdf-2410.00357">pdf</a>, <a href="https://arxiv.org/html/2410.00357v1" title="View HTML" id="html-2410.00357" aria-labelledby="html-2410.00357" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00357" title="Other formats" id="oth-2410.00357" aria-labelledby="oth-2410.00357">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+H">Hao Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Z">Zecheng Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liao,+W">Wenjing Liao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Schaeffer,+H">Hayden Schaeffer</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='item32'>[32]</a> <a href ="/abs/2410.00366" title="Abstract" id="2410.00366"> arXiv:2410.00366 </a> [<a href="/pdf/2410.00366" title="Download PDF" id="pdf-2410.00366" aria-labelledby="pdf-2410.00366">pdf</a>, <a href="https://arxiv.org/html/2410.00366v1" title="View HTML" id="html-2410.00366" aria-labelledby="html-2410.00366" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00366" title="Other formats" id="oth-2410.00366" aria-labelledby="oth-2410.00366">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Easydiagnos: a framework for accurate feature selection for automatic diagnosis in smart healthcare </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Maji,+P">Prasenjit Maji</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mondal,+A+K">Amit Kumar Mondal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mondal,+H+K">Hemanta Kumar Mondal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mohanty,+S+P">Saraju P. Mohanty</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item33'>[33]</a> <a href ="/abs/2410.00373" title="Abstract" id="2410.00373"> arXiv:2410.00373 </a> [<a href="/pdf/2410.00373" title="Download PDF" id="pdf-2410.00373" aria-labelledby="pdf-2410.00373">pdf</a>, <a href="https://arxiv.org/html/2410.00373v1" title="View HTML" id="html-2410.00373" aria-labelledby="html-2410.00373" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00373" title="Other formats" id="oth-2410.00373" aria-labelledby="oth-2410.00373">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Robust Traffic Forecasting against Spatial Shift over Years </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+H">Hongjun Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+J">Jiyuan Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pan,+T">Tong Pan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dong,+Z">Zheng Dong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+L">Lingyu Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jiang,+R">Renhe Jiang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Song,+X">Xuan Song</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Databases (cs.DB); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item34'>[34]</a> <a href ="/abs/2410.00381" title="Abstract" id="2410.00381"> arXiv:2410.00381 </a> [<a href="/pdf/2410.00381" title="Download PDF" id="pdf-2410.00381" aria-labelledby="pdf-2410.00381">pdf</a>, <a href="https://arxiv.org/html/2410.00381v1" title="View HTML" id="html-2410.00381" aria-labelledby="html-2410.00381" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00381" title="Other formats" id="oth-2410.00381" aria-labelledby="oth-2410.00381">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Generative Precipitation Downscaling using Score-based Diffusion with Wasserstein Regularization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+Y">Yuhao Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Doss-Gollin,+J">James Doss-Gollin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Balakrishnan,+G">Guha Balakrishnan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Veeraraghavan,+A">Ashok Veeraraghavan</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 19 pages, 9 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item35'>[35]</a> <a href ="/abs/2410.00385" title="Abstract" id="2410.00385"> arXiv:2410.00385 </a> [<a href="/pdf/2410.00385" title="Download PDF" id="pdf-2410.00385" aria-labelledby="pdf-2410.00385">pdf</a>, <a href="https://arxiv.org/html/2410.00385v2" title="View HTML" id="html-2410.00385" aria-labelledby="html-2410.00385" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00385" title="Other formats" id="oth-2410.00385" aria-labelledby="oth-2410.00385">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+H">Hongjun Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+J">Jiyuan Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pan,+T">Tong Pan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dong,+Z">Zheng Dong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+L">Lingyu Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jiang,+R">Renhe Jiang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Song,+X">Xuan Song</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Databases (cs.DB) </div> </div> </dd> <dt> <a name='item36'>[36]</a> <a href ="/abs/2410.00393" title="Abstract" id="2410.00393"> arXiv:2410.00393 </a> [<a href="/pdf/2410.00393" title="Download PDF" id="pdf-2410.00393" aria-labelledby="pdf-2410.00393">pdf</a>, <a href="https://arxiv.org/html/2410.00393v1" title="View HTML" id="html-2410.00393" aria-labelledby="html-2410.00393" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00393" title="Other formats" id="oth-2410.00393" aria-labelledby="oth-2410.00393">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Revisiting Essential and Nonessential Settings of Evidential Deep Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+M">Mengyuan Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gao,+J">Junyu Gao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xu,+C">Changsheng Xu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 22 pages, under review </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item37'>[37]</a> <a href ="/abs/2410.00407" title="Abstract" id="2410.00407"> arXiv:2410.00407 </a> [<a href="/pdf/2410.00407" title="Download PDF" id="pdf-2410.00407" aria-labelledby="pdf-2410.00407">pdf</a>, <a href="https://arxiv.org/html/2410.00407v2" title="View HTML" id="html-2410.00407" aria-labelledby="html-2410.00407" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00407" title="Other formats" id="oth-2410.00407" aria-labelledby="oth-2410.00407">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Intelligent Repetition Counting for Unseen Exercises: A Few-Shot Learning Approach with Sensor Signals </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Lim,+Y">Yooseok Lim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+S">Sujee Lee</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='item38'>[38]</a> <a href ="/abs/2410.00432" title="Abstract" id="2410.00432"> arXiv:2410.00432 </a> [<a href="/pdf/2410.00432" title="Download PDF" id="pdf-2410.00432" aria-labelledby="pdf-2410.00432">pdf</a>, <a href="https://arxiv.org/html/2410.00432v1" title="View HTML" id="html-2410.00432" aria-labelledby="html-2410.00432" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00432" title="Other formats" id="oth-2410.00432" aria-labelledby="oth-2410.00432">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Scalable Multi-Task Transfer Learning for Molecular Property Prediction </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+C">Chanhui Lee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jeong,+D">Dae-Woong Jeong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ko,+S+M">Sung Moon Ko</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+S">Sumin Lee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+H">Hyunseung Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yim,+S">Soorin Yim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Han,+S">Sehui Han</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+S">Sungwoong Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lim,+S">Sungbin Lim</a></div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> ICML2024-AI4Science Poster </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item39'>[39]</a> <a href ="/abs/2410.00435" title="Abstract" id="2410.00435"> arXiv:2410.00435 </a> [<a href="/pdf/2410.00435" title="Download PDF" id="pdf-2410.00435" aria-labelledby="pdf-2410.00435">pdf</a>, <a href="https://arxiv.org/html/2410.00435v3" title="View HTML" id="html-2410.00435" aria-labelledby="html-2410.00435" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00435" title="Other formats" id="oth-2410.00435" aria-labelledby="oth-2410.00435">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Incorporating Arbitrary Matrix Group Equivariance into KANs </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Hu,+L">Lexiang Hu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y">Yisen Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lin,+Z">Zhouchen Lin</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/2410.00454" title="Abstract" id="2410.00454"> arXiv:2410.00454 </a> [<a href="/pdf/2410.00454" title="Download PDF" id="pdf-2410.00454" aria-labelledby="pdf-2410.00454">pdf</a>, <a href="https://arxiv.org/html/2410.00454v1" title="View HTML" id="html-2410.00454" aria-labelledby="html-2410.00454" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00454" title="Other formats" id="oth-2410.00454" aria-labelledby="oth-2410.00454">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> UniAdapt: A Universal Adapter for Knowledge Calibration </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Nguyen,+T+D">Tai D. Nguyen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pham,+L+H">Long H. Pham</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+J">Jun Sun</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='item41'>[41]</a> <a href ="/abs/2410.00461" title="Abstract" id="2410.00461"> arXiv:2410.00461 </a> [<a href="/pdf/2410.00461" title="Download PDF" id="pdf-2410.00461" aria-labelledby="pdf-2410.00461">pdf</a>, <a href="https://arxiv.org/html/2410.00461v1" title="View HTML" id="html-2410.00461" aria-labelledby="html-2410.00461" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00461" title="Other formats" id="oth-2410.00461" aria-labelledby="oth-2410.00461">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Enhancing Solution Efficiency in Reinforcement Learning: Leveraging Sub-GFlowNet and Entropy Integration </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=He,+S">Siyi He</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='item42'>[42]</a> <a href ="/abs/2410.00509" title="Abstract" id="2410.00509"> arXiv:2410.00509 </a> [<a href="/pdf/2410.00509" title="Download PDF" id="pdf-2410.00509" aria-labelledby="pdf-2410.00509">pdf</a>, <a href="https://arxiv.org/html/2410.00509v3" title="View HTML" id="html-2410.00509" aria-labelledby="html-2410.00509" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00509" title="Other formats" id="oth-2410.00509" aria-labelledby="oth-2410.00509">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Learning Personalized Treatment Decisions in Precision Medicine: Disentangling Treatment Assignment Bias in Counterfactual Outcome Prediction and Biomarker Identification </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Vollenweider,+M">Michael Vollenweider</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sch%C3%BCrch,+M">Manuel Sch眉rch</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rohrer,+C">Chiara Rohrer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gut,+G">Gabriele Gut</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Krauthammer,+M">Michael Krauthammer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wicki,+A">Andreas Wicki</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 9 pages, 5 figures, ML4H conference 2024 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Information Theory (cs.IT); Quantitative Methods (q-bio.QM) </div> </div> </dd> <dt> <a name='item43'>[43]</a> <a href ="/abs/2410.00510" title="Abstract" id="2410.00510"> arXiv:2410.00510 </a> [<a href="/pdf/2410.00510" title="Download PDF" id="pdf-2410.00510" aria-labelledby="pdf-2410.00510">pdf</a>, <a href="https://arxiv.org/html/2410.00510v2" title="View HTML" id="html-2410.00510" aria-labelledby="html-2410.00510" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00510" title="Other formats" id="oth-2410.00510" aria-labelledby="oth-2410.00510">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Advancing RVFL networks: Robust classification with the HawkEye loss function </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Akhtar,+M">Mushir Akhtar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mishra,+R">Ritik Mishra</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tanveer,+M">M. Tanveer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Arshad,+M">Mohd. Arshad</a></div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> 31st International Conference on Neural Information Processing (ICONIP), 2024 </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='item44'>[44]</a> <a href ="/abs/2410.00524" title="Abstract" id="2410.00524"> arXiv:2410.00524 </a> [<a href="/pdf/2410.00524" title="Download PDF" id="pdf-2410.00524" aria-labelledby="pdf-2410.00524">pdf</a>, <a href="https://arxiv.org/html/2410.00524v2" title="View HTML" id="html-2410.00524" aria-labelledby="html-2410.00524" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00524" title="Other formats" id="oth-2410.00524" aria-labelledby="oth-2410.00524">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Deep Model Interpretation with Limited Data : A Coreset-based Approach </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Behzadi-Khormouji,+H">Hamed Behzadi-Khormouji</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Oramas,+J">Jos茅 Oramas</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) </div> </div> </dd> <dt> <a name='item45'>[45]</a> <a href ="/abs/2410.00535" title="Abstract" id="2410.00535"> arXiv:2410.00535 </a> [<a href="/pdf/2410.00535" title="Download PDF" id="pdf-2410.00535" aria-labelledby="pdf-2410.00535">pdf</a>, <a href="https://arxiv.org/html/2410.00535v3" title="View HTML" id="html-2410.00535" aria-labelledby="html-2410.00535" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00535" title="Other formats" id="oth-2410.00535" aria-labelledby="oth-2410.00535">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> The Causal Information Bottleneck and Optimal Causal Variable Abstractions </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Simoes,+F+N+F+Q">Francisco N. F. Q. Simoes</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dastani,+M">Mehdi Dastani</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=van+Ommen,+T">Thijs van Ommen</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Submitted to UAI 2025. Code available at <a href="http://github.com/francisco-simoes/cib-optimization-psagd" rel="external noopener nofollow" class="link-external link-http">this http 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); Information Theory (cs.IT); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item46'>[46]</a> <a href ="/abs/2410.00542" title="Abstract" id="2410.00542"> arXiv:2410.00542 </a> [<a href="/pdf/2410.00542" title="Download PDF" id="pdf-2410.00542" aria-labelledby="pdf-2410.00542">pdf</a>, <a href="/format/2410.00542" title="Other formats" id="oth-2410.00542" aria-labelledby="oth-2410.00542">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Differentially Private Active Learning: Balancing Effective Data Selection and Privacy </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Schwethelm,+K">Kristian Schwethelm</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kaiser,+J">Johannes Kaiser</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kuntzer,+J">Jonas Kuntzer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yigitsoy,+M">Mehmet Yigitsoy</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rueckert,+D">Daniel Rueckert</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kaissis,+G">Georgios Kaissis</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> This work has been accepted for publication in the IEEE Conference on Secure and Trustworthy Machine Learning (SaTML). The final version will be available on IEEE Xplore </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Cryptography and Security (cs.CR) </div> </div> </dd> <dt> <a name='item47'>[47]</a> <a href ="/abs/2410.00544" title="Abstract" id="2410.00544"> arXiv:2410.00544 </a> [<a href="/pdf/2410.00544" title="Download PDF" id="pdf-2410.00544" aria-labelledby="pdf-2410.00544">pdf</a>, <a href="https://arxiv.org/html/2410.00544v2" title="View HTML" id="html-2410.00544" aria-labelledby="html-2410.00544" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00544" title="Other formats" id="oth-2410.00544" aria-labelledby="oth-2410.00544">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Sabanza-Gil,+V">V铆ctor Sabanza-Gil</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Barbano,+R">Riccardo Barbano</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Guti%C3%A9rrez,+D+P">Daniel Pacheco Guti茅rrez</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Luterbacher,+J+S">Jeremy S. Luterbacher</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hern%C3%A1ndez-Lobato,+J+M">Jos茅 Miguel Hern谩ndez-Lobato</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Schwaller,+P">Philippe Schwaller</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Roch,+L">Lo茂c Roch</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='item48'>[48]</a> <a href ="/abs/2410.00564" title="Abstract" id="2410.00564"> arXiv:2410.00564 </a> [<a href="/pdf/2410.00564" title="Download PDF" id="pdf-2410.00564" aria-labelledby="pdf-2410.00564">pdf</a>, <a href="https://arxiv.org/html/2410.00564v2" title="View HTML" id="html-2410.00564" aria-labelledby="html-2410.00564" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00564" title="Other formats" id="oth-2410.00564" aria-labelledby="oth-2410.00564">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model Pretraining </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Cheng,+J">Jie Cheng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Qiao,+R">Ruixi Qiao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xiong,+G">Gang Xiong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Miao,+Q">Qinghai Miao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ma,+Y">Yingwei Ma</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+B">Binhua Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+Y">Yongbin Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lv,+Y">Yisheng Lv</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item49'>[49]</a> <a href ="/abs/2410.00645" title="Abstract" id="2410.00645"> arXiv:2410.00645 </a> [<a href="/pdf/2410.00645" title="Download PDF" id="pdf-2410.00645" aria-labelledby="pdf-2410.00645">pdf</a>, <a href="https://arxiv.org/html/2410.00645v1" title="View HTML" id="html-2410.00645" aria-labelledby="html-2410.00645" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00645" title="Other formats" id="oth-2410.00645" aria-labelledby="oth-2410.00645">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> ICL-TSVD: Bridging Theory and Practice in Continual Learning with Pre-trained Models </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Peng,+L">Liangzu Peng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Elenter,+J">Juan Elenter</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Agterberg,+J">Joshua Agterberg</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ribeiro,+A">Alejandro Ribeiro</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Vidal,+R">Ren茅 Vidal</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 45 pages, 19 figures, 14 tables (Preprint, Oct 1, 2024) </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='item50'>[50]</a> <a href ="/abs/2410.00655" title="Abstract" id="2410.00655"> arXiv:2410.00655 </a> [<a href="/pdf/2410.00655" title="Download PDF" id="pdf-2410.00655" aria-labelledby="pdf-2410.00655">pdf</a>, <a href="https://arxiv.org/html/2410.00655v1" title="View HTML" id="html-2410.00655" aria-labelledby="html-2410.00655" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00655" title="Other formats" id="oth-2410.00655" aria-labelledby="oth-2410.00655">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> AutoTM 2.0: Automatic Topic Modeling Framework for Documents Analysis </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Khodorchenko,+M">Maria Khodorchenko</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Butakov,+N">Nikolay Butakov</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zuev,+M">Maxim Zuev</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nasonov,+D">Denis Nasonov</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) </div> </div> </dd> </dl> <div class='paging'>Total of 4843 entries : <span>1-50</span> <a href=/list/cs.LG/2024-10?skip=50&show=50>51-100</a> <a href=/list/cs.LG/2024-10?skip=100&show=50>101-150</a> <a href=/list/cs.LG/2024-10?skip=150&show=50>151-200</a> <span>...</span> <a href=/list/cs.LG/2024-10?skip=4800&show=50>4801-4843</a> </div> <div class='morefewer'>Showing up to 50 entries per page: <a href=/list/cs.LG/2024-10?skip=0&show=25 rel="nofollow"> fewer</a> | <a href=/list/cs.LG/2024-10?skip=0&show=100 rel="nofollow"> more</a> | <a href=/list/cs.LG/2024-10?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"> <!-- Macro-Column 1 --> <div class="column" style="padding: 0;"> <div class="columns"> <div class="column"> <ul style="list-style: none; line-height: 2;"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul style="list-style: none; 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