{"id":2968,"date":"2019-12-26T06:35:00","date_gmt":"2019-12-26T06:35:00","guid":{"rendered":"https:\/\/www.aiproblog.com\/index.php\/2019\/12\/26\/neurips-2019-analysis-of-papers-by-themes\/"},"modified":"2019-12-26T06:35:00","modified_gmt":"2019-12-26T06:35:00","slug":"neurips-2019-analysis-of-papers-by-themes","status":"publish","type":"post","link":"https:\/\/www.aiproblog.com\/index.php\/2019\/12\/26\/neurips-2019-analysis-of-papers-by-themes\/","title":{"rendered":"NeurIPS 2019 \u2013 analysis of papers by themes"},"content":{"rendered":"<p>Author: ajit jaokar<\/p>\n<div>\n<p><a href=\"https:\/\/storage.ning.com\/topology\/rest\/1.0\/file\/get\/3786624550?profile=original\" target=\"_blank\" rel=\"noopener noreferrer\"><img decoding=\"async\" src=\"https:\/\/storage.ning.com\/topology\/rest\/1.0\/file\/get\/3786624550?profile=RESIZE_710x\" class=\"align-full\"><\/a><\/p>\n<p>You can always learn a lot from the papers presented at <a href=\"https:\/\/nips.cc\/\">NeurIPS<\/a><\/p>\n<p>There is some good analysis already on the web.<\/p>\n<p>From <a href=\"https:\/\/huyenchip.com\/2019\/12\/18\/key-trends-neurips-2019.html\">Chip Huygen \u2013 neurips 2019<\/a> analysis and from <a href=\"https:\/\/david-abel.github.io\/notes\/neurips_2019.pdf\">David Abel neurips 2019 analysis<\/a><\/p>\n<p>Most major players were also well represented at NeurIPS including\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/p>\n<p><a href=\"https:\/\/research.fb.com\/conferences\/neurips-2019\/\">Facebook at neurips 2019<\/a><\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/neurips-2019\/\">Microsoft at neurips 2019<\/a><\/p>\n<p><a href=\"https:\/\/ai.googleblog.com\/2019\/12\/google-at-neurips-2019.html\">Google at neurips 2019\u00a0<\/a><\/p>\n<p><a href=\"https:\/\/www.intel.ai\/events\/neurips-2019\/\">Intel at neurips 2019<\/a><\/p>\n<p><a href=\"https:\/\/blogs.unity3d.com\/2019\/11\/29\/accelerating-ml-research-meet-us-at-neurips-2019\/\">Unity 3d at neurips 2019<\/a><\/p>\n<p><a href=\"https:\/\/www.ibm.com\/blogs\/research\/2019\/12\/ibm-research-ai-neurips-2019\/\">IBM at neurips 2019<\/a><\/p>\n<p><a href=\"https:\/\/machinelearning.apple.com\/2019\/12\/02\/apple-at-neurips-2019.html\">Apple at neurips 2019<\/a><\/p>\n<p>For my students at <a href=\"https:\/\/www.conted.ox.ac.uk\/courses\/artificial-intelligence-cloud-and-edge-implementations\">University of Oxford #AI #Cloud #Edge<\/a> I did an analysis of <a href=\"https:\/\/nips.cc\/Conferences\/2019\/Schedule\">neurips papers by theme based on the neurips 2019 schedule.<\/a> I found it easier to analyse papers based on theme<\/p>\n<p>The themes covered in NeurIPS were<\/p>\n<p><strong>Algorithms<\/strong><\/p>\n<ul>\n<li>Adaptive Data Analysis<\/li>\n<li>Adversarial Learning<\/li>\n<li>Bandit Algorithms<\/li>\n<li>Boosting and Ensemble Methods<\/li>\n<li>Clustering<\/li>\n<li>Components Analysis (e.g., CCA, ICA, LDA, PCA)<\/li>\n<li>Density Estimation<\/li>\n<li>Dynamical Systems<\/li>\n<li>Kernel Methods<\/li>\n<li>Meta-Learning \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/li>\n<li>Missing Data<\/li>\n<li>Model Selection and Structure Learning \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/li>\n<li>Regression<\/li>\n<li>Representation Learning<\/li>\n<li>Semi-Supervised Learning<\/li>\n<li>Similarity and Distance Learning<\/li>\n<li>Structured Prediction<\/li>\n<li>Uncertainty Estimation<\/li>\n<li>Unsupervised Learning<\/li>\n<\/ul>\n<p><strong>Applications<\/strong><\/p>\n<ul>\n<li>Body Pose, Face, and Gesture Analysis<\/li>\n<li>Communication- or Memory-Bounded Learning<\/li>\n<li>Computer Vision \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/li>\n<li>Dialog- or Communication-Based Learning<\/li>\n<li>Game Playing<\/li>\n<li>Image Segmentation<\/li>\n<li>Object Detection<\/li>\n<li>Privacy, Anonymity, and Security<\/li>\n<li>Recommender Systems\u00a0\u00a0\u00a0\u00a0<\/li>\n<li>Robotics\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/li>\n<li>Web Applications and Internet Data<\/li>\n<li>Biologically Plausible Deep Networks<\/li>\n<\/ul>\n<p><strong>Deep Learning<\/strong><\/p>\n<ul>\n<li>Deep Autoencoders<\/li>\n<li>Efficient Inference Methods<\/li>\n<li>Generative Models \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/li>\n<li>Interaction-Based Deep Networks<\/li>\n<li>Optimization for Deep Networks \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/li>\n<li>Predictive Models<\/li>\n<li>Recurrent Networks \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/li>\n<li>Visualization or Exposition Techniques for Deep Networks \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/li>\n<\/ul>\n<p><strong>Optimization<\/strong><\/p>\n<ul>\n<li>Combinatorial Optimization<\/li>\n<li>Convex Optimization \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/li>\n<li>Non-Convex Optimization<\/li>\n<li>Stochastic Optimization<\/li>\n<\/ul>\n<p><strong>Probabilistic Methods<\/strong><\/p>\n<ul>\n<li>Causal Inference<\/li>\n<li>Distributed Inference<\/li>\n<li>Gaussian Processes<\/li>\n<li>Hierarchical Models<\/li>\n<li>MCMC<\/li>\n<li>Variational Inference<\/li>\n<\/ul>\n<p><strong>Reinforcement Learning and Planning<\/strong><\/p>\n<ul>\n<li>Decision and Control<\/li>\n<li>Exploration<\/li>\n<li>Markov Decision Processes<\/li>\n<li>Model-Based RL<\/li>\n<li>Multi-Agent RL<\/li>\n<li>Navigation<\/li>\n<li>Reinforcement Learning<\/li>\n<\/ul>\n<p><strong>Theory<\/strong><\/p>\n<ul>\n<li>Computational Complexity<\/li>\n<li>Control Theory<\/li>\n<li>Frequentist Statistics<\/li>\n<li>Hardness of Learning and Approximations<\/li>\n<li>Learning Theory<\/li>\n<\/ul>\n<p>A full list of papers by theme below<\/p>\n<p><a href=\"https:\/\/docs.google.com\/document\/d\/1cyetjKhK1SuIVPYn_5Sh_UIVw-jiOCBgytay3smmjp0\/edit?usp=sharing\" target=\"_blank\" rel=\"noopener noreferrer\">NeurIPS 2019 analysis of papers by theme<\/a><\/p>\n<\/p>\n<p>\u00a0<\/p>\n<\/p>\n<p>\u00a0<\/p>\n<p>Image source: <a href=\"https:\/\/research.yandex.com\/news\/papers-accepted-to-neurips-2019\">Yandex @neurips<\/a><\/p>\n<\/div>\n<p><a href=\"https:\/\/www.datasciencecentral.com\/xn\/detail\/6448529:BlogPost:917455\">Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Author: ajit jaokar You can always learn a lot from the papers presented at NeurIPS There is some good analysis already on the web. From [&hellip;] <span class=\"read-more-link\"><a class=\"read-more\" href=\"https:\/\/www.aiproblog.com\/index.php\/2019\/12\/26\/neurips-2019-analysis-of-papers-by-themes\/\">Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":461,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[26],"tags":[],"_links":{"self":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/2968"}],"collection":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/comments?post=2968"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/2968\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/462"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=2968"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=2968"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=2968"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}