{"id":13790205,"url":"https://github.com/kumarkrishna/paper-spray","last_synced_at":"2026-01-24T16:18:39.561Z","repository":{"id":74933795,"uuid":"59034542","full_name":"kumarkrishna/paper-spray","owner":"kumarkrishna","description":"List of interesting papers to read","archived":false,"fork":false,"pushed_at":"2017-09-04T19:42:57.000Z","size":276,"stargazers_count":64,"open_issues_count":3,"forks_count":20,"subscribers_count":9,"default_branch":"master","last_synced_at":"2024-11-18T04:35:50.181Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kumarkrishna.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2016-05-17T15:24:54.000Z","updated_at":"2023-08-21T18:30:42.000Z","dependencies_parsed_at":"2023-02-26T05:15:57.665Z","dependency_job_id":null,"html_url":"https://github.com/kumarkrishna/paper-spray","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kumarkrishna%2Fpaper-spray","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kumarkrishna%2Fpaper-spray/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kumarkrishna%2Fpaper-spray/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kumarkrishna%2Fpaper-spray/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kumarkrishna","download_url":"https://codeload.github.com/kumarkrishna/paper-spray/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253695183,"owners_count":21948826,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-03T22:00:38.778Z","updated_at":"2026-01-24T16:18:39.548Z","avatar_url":"https://github.com/kumarkrishna.png","language":"Python","funding_links":[],"categories":["论文集合"],"sub_categories":["其他"],"readme":"# Paper-Spray\n\nThis is a list of interesting research papers started by\n[Kumar](https://github.com/kumarkrishna) and [Biswa](https://github.com/biswajitsc) (currently being maintained only by Kumar),\nmainly in Machine Learning, but definitely not limited to it.\nThis is mainly an initiative to inculcate a reading habit among ourselves.\nSuggested reads are always welcome!\n\nWe would try submit only links which are freely available, but we may also add\nfew links which can be accessed freely only from an university network.\n\n__We have created a webpage for [Paper-Spray](https://biswajitsc.github.io/paper-spray.html)\ncontaining a searchable list of the papers in the json file.__\n\nEntry format:\n\u003e * \u003ca href=\"link\"\u003ePaper Title\u003c/a\u003e\n\u003e ```Date Added, Keywords```\n\u003e ```Author, Conference, Year```\n\nAbbreviations:\n* AI: Artificial Intelligence\n* CV : Computer Vision\n* DL: Deep Learning\n* ML : Machine Learning\n* NLP : Natural Language Processing\n* RL : Reinforcement Learning\n\n## How it works?\nThe papers are added to ```paper-list.json```. They can either be added\nmanually or by using the ```add_papers.py``` script. Thereafter the README is\ngenerated by using the ```create_readme.py``` script. This script appends the paper\nnames present in the json file to the contents of ```readme.template```,\nto generate ```README.md```.\n\nSome scripts such as ```add_papers.sh``` and ```add_papers_minimal.sh``` have\nbeen created for convenience.\n\nThe scripts give a warning when adding duplicate papers. In that case,\nenter 'n' when asked to abort adding the paper.\n\nThe webpage for paper-spray reads the json file and creates a table using js libraries.\nThere is no need of generating static html pages for any change in the json file.\n\n\u003c!---\nCLI for adding papers :\n* Add ```$paperspraypath``` as environment variable for path to the github repository.\n```sh\nexport paperspraypath=/path/to/github/repository\n```\n* Add an alias to .bashrc / .bash_profile to directly add papers from any folder through terminal :D .\n```sh\nalias spray-papers=\"bash $paperspraypath/scripts/add_papers.sh\"\n```\n * Use ```spray-papers``` as terminal command.\n--\u003e\n\n\n## Papers\n* \u003ca href=\"https://arxiv.org/abs/1708.05144\"\u003eScalable trust-region method for deep reinforcement learning using Kronecker-factored approximation\u003c/a\u003e  \n```17/08/2017, RL, K-FAC```  \n```Yuhuai Wu, Elman Mansimov, Shun Liao, Roger Grosse, Jimmy Ba, arXiv``` \u003ca href=\"https://blog.openai.com/baselines-acktr-a2c/\"\u003e\\[Review\\]\u003c/a\u003e  \n* \u003ca href=\"https://books.google.com/books/about/Function_Optimization_Using_Connectionis.html?id=g9TLGwAACAAJ\"\u003eFunction Optimization Using Connectionist Reinforcement Learning Algorithms\u003c/a\u003e  \n```16/08/2017, RL```  \n```Ronald J. Williams, Jing Peng, Connection Science```   \n* \u003ca href=\"https://arxiv.org/abs/1703.00887\"\u003eHow to Escape Saddle Points Efficiently\u003c/a\u003e  \n```16/08/2017, ML, Non-Convex```  \n```Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan, ICML 2017``` \u003ca href=\"http://www.offconvex.org/2017/07/19/saddle-efficiency/\"\u003e\\[Review\\]\u003c/a\u003e  \n* \u003ca href=\"https://arxiv.org/abs/1707.02286\"\u003eEmergence of Locomotion Behaviours in Rich Environments\u003c/a\u003e  \n```16/07/2017, RL, Robotics, PPO```  \n```Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, S. M. Ali Eslami, Martin Riedmiller, David Silver, arXiv```   \n* \u003ca href=\"https://arxiv.org/abs/1509.02971\"\u003eContinuous control with deep reinforcement learning\u003c/a\u003e  \n```16/07/2017, RL, DDPG```  \n```Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra, ICLR 2016```   \n* \u003ca href=\"https://arxiv.org/abs/1502.05477\"\u003eTrust Region Policy Optimization\u003c/a\u003e  \n```16/07/2017, RL, Robotics```  \n```John Schulman, Sergey Levine, Philipp Moritz, Michael Jorda, Pieter Abbeel, ICML, 2015```   \n* \u003ca href=\"https://arxiv.org/abs/1707.04585\"\u003eThe Reversible Residual Network: Backpropagation Without Storing Activations\u003c/a\u003e  \n```14/06/2017, CV, RevNets```  \n```Aidan N. Gomez, Mengye Ren, Raquel Urtasun, Roger B. Grosse, arXiv```   \n* \u003ca href=\"http://www.jmlr.org/proceedings/papers/v48/cutajar16.pdf\"\u003ePreconditioning Kernel Matrices\u003c/a\u003e  \n```15/12/2016, kernel methods```  \n```Kurt Cutajar, Michael Osborne, John Cunningham, Maurizio Filippone, ICML 2016```   \n* \u003ca href=\"https://arxiv.org/abs/1611.07004v1\"\u003eImage-to-Image Translation with Conditional Adversarial Networks\u003c/a\u003e  \n```15/12/2016, CV, DL, GAN```  \n```Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A.Efros, arxiv```   \n* \u003ca href=\"https://arxiv.org/abs/1509.02971\"\u003eContinous Control with Deep Reinforcement Learning\u003c/a\u003e  \n```15/12/2016, DL, RL, DDPG```  \n```Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra, ICLR 2016```   \n* \u003ca href=\"https://arxiv.org/abs/1612.00005\"\u003ePlug \u0026 Play Generative Networks: Conditional Iterative Generation of Images in Latent Space\u003c/a\u003e  \n```04/12/2016, generative model, latent space```  \n```Anh Nguyen, Jason Yosinski, Yoshua Bengio, Alexey Dosovitskiy, Jeff Clune, arXiv```   \n* \u003ca href=\"https://arxiv.org/abs/1611.09961\"\u003eSemantic Facial Expression Editing using Autoencoded Flow\u003c/a\u003e  \n```04/12/2016, autoencoder, latent space, image manipulation```  \n```Raymond Yeh, Ziwei Liu, Dan B Goldman, Aseem Agarwala, arXiv```   \n* \u003ca href=\"https://arxiv.org/abs/1611.00035\"\u003eFull-Capacity Unitary Recurrent Neural Networks\u003c/a\u003e  \n```02/11/2016, DL```  \n```Scott Wisdom, Thomas Powers, John R. Hershey, Jonathan Le Roux, Les Atlas, NIPS 2016```   \n* \u003ca href=\"https://arxiv.org/abs/1610.09585\"\u003eConditional Image Synthesis With Auxiliary Classifier GANs\u003c/a\u003e  \n```02/11/2016, CV, DL, GAN```  \n```Augustus Odena, Christopher Olan, Jonatho Shlens, arxiv```   \n* \u003ca href=\"https://arxiv.org/abs/1611.00336\"\u003eStochastic Variational Deep Kernel Learning\u003c/a\u003e  \n```02/11/2016, DL, ML```  \n```Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing, NIPS 2016```   \n* \u003ca href=\"https://arxiv.org/abs/1511.07122\"\u003eMulti-Scale Context Aggregation by Dilated Convolutions\u003c/a\u003e  \n```01/11/2016, CV, DL```  \n```Fisher Yu, Vladlen Klotun, ICLR 2016```   \n* \u003ca href=\"https://arxiv.org/abs/1610.10099\"\u003eNeural Machine Translation in Linear Time\u003c/a\u003e  \n```01/11/2016, NMT, DL, dilated-convolutions```  \n```Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aaron van den Oord, Alex Graves, Koray Kavukcuoglu, arxiv```   \n* \u003ca href=\"https://arxiv.org/pdf/1607.03474v3.pdf\"\u003eRecurrent Highway Networks\u003c/a\u003e  \n```01/11/2016, DL, RNN```  \n```Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutnik, Jurgen Schmidhuber, arxiv```   \n* \u003ca href=\"https://arxiv.org/pdf/1610.08466v1.pdf\"\u003eRecurrent Switching Linear Dynamical Systems\u003c/a\u003e  \n```31/10/2016, ML```  \n```Scott Linderman, Andrew Miller, Ryan Adams, David Blei, Liam Paninski, Matthew Johnson, arxiv```   \n* \u003ca href=\"https://arxiv.org/abs/1610.09033\"\u003eOperator Variational Inference\u003c/a\u003e  \n```31/10/2016, ML, variational```  \n```Rajesh Ranganath, Jaan ALtosaar, Dustin Tran, David M. Blei, arxiv```   \n* \u003ca href=\"https://arxiv.org/abs/1610.09038\"\u003eProfessor Forcing: A New Algorithm for Training Recurrent Networks\u003c/a\u003e  \n```31/10/2016, DL, RNN```  \n```Alex Lamb, Anirudh Goyal, Ying Zhang, Saizheng Zhang, Aaron Courville, Yoshua Bengio, NIPS 2016```   \n* \u003ca href=\"https://arxiv.org/pdf/1609.07843.pdf\"\u003ePointer Sentinel Mixture Models\u003c/a\u003e  \n```30/10/2016, DL, NLP```  \n```Stephen Merity, Caiming Xiong, James Bradbury, Richard Socher, rxiv```   \n* \u003ca href=\"https://arxiv.org/abs/1610.08613\"\u003eCan Active Memory Replace Attention?\u003c/a\u003e  \n```28/10/2016, DL```  \n```Lukasz Kaiser, Samy Bengio, NIPS 2016```   \n* \u003ca href=\"http://jmlr.org/proceedings/papers/v23/agrawal12/agrawal12.pdf\"\u003eAnalysis of Thompson Sampling for the Multi-armed Bandit Problem\u003c/a\u003e  \n```25/10/2016, Sampling, Bandits```  \n```Shipra Agrawal, Navin Goyal, JMLR 2012```   \n* \u003ca href=\"https://arxiv.org/abs/1507.00814\"\u003eIncentivizing Exploration In Reinforcement Learning With Deep Predictive Models\u003c/a\u003e  \n```25/10/2016, DL, RL```  \n```Bradly Stadie, Sergey Levine, Pieter Abbeel, arxiv```   \n* \u003ca href=\"https://arxiv.org/abs/1502.05336\"\u003eProbabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks\u003c/a\u003e  \n```22/10/2016, DL, optimization, bayesian```  \n```José Miguel Hernández-Lobato, Ryan P. Adams, JMLR```   \n* \u003ca href=\"https://arxiv.org/pdf/1609.05518v2.pdf\"\u003eTowards Deep Symbolic Reinforcement Learning\u003c/a\u003e  \n```21/10/2016, DL, RL```  \n```Marta Garnelo, Kai Arulkumaran, Murray Shanahan, arxiv```   \n* \u003ca href=\"https://arxiv.org/abs/1607.06450\"\u003eLayer Normalization\u003c/a\u003e  \n```21/10/2016, DL, optimization```  \n```Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton, arXiv```   \n* \u003ca href=\"https://arxiv.org/abs/1602.03264\"\u003eA Theory of Generative ConvNet\u003c/a\u003e  \n```21/10/2016, ML, statistics, generative, cnn```  \n```Jianwen Xie, Yang Lu, Song-Chun Zhu, Ying Nian Wu, ICML 2016```   \n* \u003ca href=\"https://arxiv.org/abs/1611.01796\"\u003eModular Multitask Reinforcement Learning with Policy Sketches\u003c/a\u003e  \n```06/10/2016, RL, policy sketch```  \n```Jacob Andreas, Dan Klein, Sergey Levine, ICML 2017```   \n* \u003ca href=\"http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf\"\u003eA Tutorial on Energy-Based Learning\u003c/a\u003e  \n```27/09/2016, ML, energy models```  \n```Yann LeCun, Sumit Chopra, Raia Hadsell, Marc’Aurelio Ranzato, and Fu Jie Huang, ```   \n* \u003ca href=\"http://arxiv.org/abs/1609.05473\"\u003eSeqGAN: Sequence Generative Adversarial Nets with Policy Gradient\u003c/a\u003e  \n```21/09/2016, GAN, sequence generation, policy gradient```  \n```Lantao Yu, Weinan Zhang, Jun Wang, Yong Yu, arXiv```   \n* \u003ca href=\"http://arxiv.org/abs/1609.04802v1\"\u003ePhoto-Realistic Single Image Super-Resolution Using a Generative Adversarial Network\u003c/a\u003e  \n```16/09/2016, CV, GAN, super resolution```  \n```Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi, arXiv```   \n* \u003ca href=\"http://arxiv.org/abs/1609.03126\"\u003eEnergy-based Generative Adversarial Network\u003c/a\u003e  \n```16/09/2016, GAN, DL, generative model, energy function```  \n```Junbo Zhao, Michael Mathieu, Yann LeCun, arXiv```   \n* \u003ca href=\"http://web.mit.edu/vondrick/tinyvideo/\"\u003eGenerating Videos with Scene Dynamics\u003c/a\u003e  \n```15/09/2016, CV, DL, GAN```  \n```Carl Vondrick, Hamed Pirsiavash, Antonio Torralba, NIPS 2016```   \n* \u003ca href=\"https://people.eecs.berkeley.edu/~junyanz/projects/gvm/\"\u003eGenerative Visual Manipulation on the Natural Image Manifold\u003c/a\u003e  \n```15/09/2016, CV, DL, GAN```  \n```Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman and Alexei A. Efros, ECCV 2016```   \n* \u003ca href=\"https://arxiv.org/abs/1608.08225\"\u003eWhy does deep and cheap learning work so well?\u003c/a\u003e  \n```10/09/2016, DL, ML, physics```  \n```Henry W. Lin, Max Tegmark, arXiv```   \n* \u003ca href=\"http://arxiv.org/abs/1609.00150\"\u003eReward Augmented Maximum Likelihood for Neural Structured Prediction\u003c/a\u003e  \n```01/09/2016, DL, RL, MLE```  \n```Mohammad Norouzi, Samy Bengio, Zhifeng Chen, Navdeep Jaitly, Mike Schuster, Yonghui Wu, Dale Schuurmans, NIPS 2016```   \n* \u003ca href=\"http://arxiv.org/pdf/1608.06993.pdf\"\u003eDensely Connected Convolutional Networks\u003c/a\u003e  \n```28/08/2016, DL, CNN```  \n```Gao Huang, Zhuang Liu, Kilian Q. Weinberger, arXiv```   \n* \u003ca href=\"http://arxiv.org/abs/1608.04980\"\u003eMollifying Networks\u003c/a\u003e  \n```18/08/2016, ML, optimization```  \n```Caglar Gulcehre, Marcin Moczulski, Francesco Visin, Yoshua Bengio, arXiv```   \n* \u003ca href=\"https://arxiv.org/abs/1606.04838\"\u003eOptimization Methods for Large-Scale Machine Learning\u003c/a\u003e  \n```18/08/2016, ML, optimization```  \n```Léon Bottou, Frank E. Curtis, Jorge Nocedal, arXiv```   \n* \u003ca href=\"http://arxiv.org/abs/1608.00182\"\u003eDeep FisherNet for Object Classification\u003c/a\u003e  \n```02/08/2016, CV, DL, object classification```  \n```Peng Tang, Xinggang Wang, Baoguang Shi, Xiang Bai, Wenyu Liu, Zhuowen Tu, arXiv```   \n* \u003ca href=\"https://arxiv.org/pdf/1607.04423v2.pdf\"\u003eAttention-over-Attention Neural Networks for Reading Comprehension\u003c/a\u003e  \n```26/07/2016, DL, NLP, Attention memory```  \n```Yiming Cui, Zhipeng Chen, Si Wei, arXiv```   \n* \u003ca href=\"http://arxiv.org/abs/1511.00363\"\u003eBinaryConnect : Training Deep Neural Networks with binary weights during propagations\u003c/a\u003e  \n```21/07/2016, DL, binary-connect```  \n```Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David, NIPS 2015```   \n* \u003ca href=\"https://arxiv.org/abs/1401.4082\"\u003eStochastic backpropagation and approximate inference in deep generative models\u003c/a\u003e  \n```20/07/2016, generative-models```  \n```Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra, ICML 2014```   \n* \u003ca href=\"https://arxiv.org/abs/1410.6460\"\u003eMarkov Chain Monte Carlo and Variational Inference: Bridging the Gap\u003c/a\u003e  \n```20/07/2016, MCMC, VAE```  \n```Tim Salimans, Diederik P. Kingma, Max Welling, ICML 2015```   \n* \u003ca href=\"http://arxiv.org/abs/1602.05908v1\"\u003eEfficient approaches for escaping higher order saddle points in non-convex optimization\u003c/a\u003e  \n```19/07/2016, ML, non-convex-optimization```  \n```Anima Anandkumar, Rong Ge, COLT 2016```   \n* \u003ca href=\"https://arxiv.org/pdf/1606.01549v1.pdf\"\u003eGated-Attention Readers for Text Comprehension\u003c/a\u003e  \n```19/07/2016, DL, NLP```  \n```Bhuwan Dhingra, Hanxiao Liu, William W. Cohen, Ruslan Salakhutdinov, arXiv```   \n* \u003ca href=\"http://arxiv.org/abs/1210.7559\"\u003eTensor decompositions for learning latent variable models\u003c/a\u003e  \n```19/07/2016, ML, TF```  \n```Anima Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade, Matus Telgarsky, JMLR 2014```   \n* \u003ca href=\"https://arxiv.org/abs/1601.06759\"\u003ePixel Recurrent Neural Networks\u003c/a\u003e  \n```19/07/2016, DL, RNN```  \n```Aaron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu, ICML 2016```   \n* \u003ca href=\"https://arxiv.org/abs/1410.8516\"\u003eNICE: Non-linear Independent Components Estimation\u003c/a\u003e  \n```18/07/2016, DL```  \n```Laurent Dinh, David Krueger, Yoshua Bengio, ICLR 2015```   \n* \u003ca href=\"https://papers.nips.cc/paper/901-higher-order-statistical-decorrelation-without-information-loss.pdf\"\u003eHigher Order Statistical Decorrelation without Information Loss\u003c/a\u003e  \n```18/07/2016, DL, IT```  \n```Gustavo Deco, Wilfried Brauer, NIPS 1995```   \n* \u003ca href=\"https://arxiv.org/pdf/1411.1784v1.pdf\"\u003eConditional Generative Aversarial Nets\u003c/a\u003e  \n```14/07/2016, GAN, DL```  \n```Mehdi Mirza, Simon Osindero, NIPS DL Workshop, 2014```   \n* \u003ca href=\"http://arxiv.org/pdf/1512.01337v4.pdf\"\u003eNeural Generative Question Answering\u003c/a\u003e  \n```14/07/2016, DL, NLP, QA```  \n```Jun Yin, Xin Jiang, Zhengdong Lu, Lifeng Shang, Hang Li, Xiaoming Li, ICLR 2016```   \n* \u003ca href=\"http://arxiv.org/pdf/1606.01933.pdf\"\u003eA Decomposable Attention Model for Natural Language Inference\u003c/a\u003e  \n```09/07/2016, DL, NLP```  \n```Ankur P. Parikh, Oscar Tackstrom, Dipanjan Das, Jakob Uszkoreit, arXiv```   \n* \u003ca href=\"http://arxiv.org/abs/1511.06732\"\u003eSequence Level Training with Recurrent Neural Networks\u003c/a\u003e  \n```06/07/2016, DL, RNN```  \n```Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, Wojciech Zaremba, ICLR 2016```   \n* \u003ca href=\"http://people.csail.mit.edu/khosla/papers/nips2012_khosla.pdf\"\u003eMemorability of Image Regions\u003c/a\u003e  \n```04/07/2016, CV, DL```  \n```Aditya Khosla, Jianxiong Xiao, Antonio Torralba, Aude Oliva, NIPS 2012```   \n* \u003ca href=\"http://arxiv.org/pdf/1508.06615v4.pdf\"\u003eCharacter-Aware Neural Language Models\u003c/a\u003e  \n```04/07/2016, DL, NLP```  \n```Yoon Kim, Yacine Jernite, David Sontag, Alexander M. Rush, AAAI 2016```   \n* \u003ca href=\"http://arxiv.org/pdf/1606.02447.pdf\"\u003eLearning Language Games through Interaction\u003c/a\u003e  \n```03/07/2016, DL, NLP```  \n```Sida Wang, Percy Liang, Chris Manning, ACL 2016```   \n* \u003ca href=\"http://arxiv.org/pdf/1412.6856.pdf\"\u003eObject Detectors emerge in Deep Scene CNNs\u003c/a\u003e  \n```02/07/2016, CV, DL```  \n```Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba, ICLR 2015```   \n* \u003ca href=\"https://arxiv.org/pdf/1502.02476v4.pdf\"\u003eAn Infinite Restricted Boltzmann Machine\u003c/a\u003e  \n```01/07/2016, ML, RBM```  \n```Marc-Alexandre Cote, Hugo Larochelle, Neural Computation```   \n* \u003ca href=\"http://arxiv.org/abs/1505.01596\"\u003eLearning to See by Moving\u003c/a\u003e  \n```30/06/2016, CV, DL```  \n```Pulkit Agrawal, Joao Carreira, Jitendra Malik, ICCV 2015```   \n* \u003ca href=\"http://arxiv.org/abs/1412.3773\"\u003eDistinguishing cause from effect using observational data: methods and benchmarks\u003c/a\u003e  \n```29/06/2016, ML, cause-inference```  \n```Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Scholkopf, JMLR 2016```   \n* \u003ca href=\"http://arxiv.org/pdf/1511.06038.pdf\"\u003eNeural Variational Inference for Text Processing\u003c/a\u003e  \n```29/06/2016, DL, NLP```  \n```Yishu Miao, Lei Yu, Phil Blunsom, arXiv```   \n* \u003ca href=\"https://arxiv.org/pdf/1506.02516.pdf\"\u003eLearning to Transduce with Unbounded Memory\u003c/a\u003e  \n```27/06/2016, DL, NTM, neural data structures```  \n```Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, Phil Blunsom, NIPS 2015```   \n* \u003ca href=\"http://cmp.felk.cvut.cz/cmp/courses/EP33VKR/2007/Cheng-PAMI1995.pdf\"\u003eMean Shift, Mode Seeking, and Clustering\u003c/a\u003e  \n```26/06/2016, ML, Clustering```  \n```Yizong Cheng, IEEE, 1995```   \n* \u003ca href=\"http://papers.nips.cc/paper/3319-adaptive-online-gradient-descent.pdf\"\u003eAdaptive Online Gradient Descent\u003c/a\u003e  \n```25/06/2016, optimization, gradient descent```  \n```Peter L. Bartlett, Elad Hazan, Alexander Rakhlin, NIPS 2007```   \n* \u003ca href=\"https://visualgenome.org/static/paper/Visual_Genome.pdf\"\u003eVisual Genome\u003c/a\u003e  \n```24/06/2016, vision, nlp multimodal dataset```  \n```Ranjay Krishna et. al., Dataset```   \n* \u003ca href=\"http://arxiv.org/pdf/1511.07404.pdf\"\u003eLearning Visual Predictive Models of Physics for Playing Billiards\u003c/a\u003e  \n```23/06/2016, CV, DL```  \n```Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, Jitendra Malik, ICLR 2016```   \n* \u003ca href=\"http://arxiv.org/abs/1606.05908\"\u003eTutorial on Variational Autoencoders\u003c/a\u003e  \n```22/06/2016, DL, VAE```  \n```Carl Doersch, arXiv```   \n* \u003ca href=\"https://arxiv.org/abs/1604.08772\"\u003eTowards Conceptual Compression\u003c/a\u003e  \n```22/06/2016, DL```  \n```Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra, arXiv```   \n* \u003ca href=\"http://arxiv.org/abs/1603.01417\"\u003eDynamic Memory Networks for Visual and Textual Question Answering\u003c/a\u003e  \n```22/06/2016, CV, DL, NLP, MemNets```  \n```Caiming Xiong, Stephen Merity, Richard Socher, ICML 2016```   \n* \u003ca href=\"http://arxiv.org/abs/1511.06432\"\u003eDelving Deeper into Convolutional Networks for Learning Video Representations\u003c/a\u003e  \n```22/06/2016, CV, DL , videos```  \n```Nicolas Ballas, Li Yao, Chris Pal, Aaron Courville, ICLR 2016```   \n* \u003ca href=\"http://arxiv.org/abs/1502.08029\"\u003eDescribing Videos by Exploiting Temporal Structure\u003c/a\u003e  \n```22/06/2016, CV, DL, video```  \n```Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, Aaron Courville, ICCV 2015```   \n* \u003ca href=\"http://arxiv.org/abs/1606.03476\"\u003eGenerative Adversarial Imitation Learning\u003c/a\u003e  \n```21/06/2016, DL, generative```  \n```Jonathan Ho, Stefano Ermon, arXiv```   \n* \u003ca href=\"https://arxiv.org/pdf/1606.00709.pdf\"\u003ef-GAN: Training Generative Neural Samplers using Variational Divergence Minimization\u003c/a\u003e  \n```21/06/2016, DL, GAN, f-GAN```  \n```Sebastian Nowozin, Botond Cseke, Ryota Tomioka, arXiv```   \n* \u003ca href=\"http://arxiv.org/abs/1505.05770\"\u003eVariational Inference with Normalizing Flows\u003c/a\u003e  \n```21/06/2016, DL, VAE, inference```  \n```Danilo Jimenez Rezende, Shakir Mohamed, ICML 2015```   \n* \u003ca href=\"http://arxiv.org/abs/1506.02216\"\u003eA Recurrent Latent Variable Model for Sequential Data\u003c/a\u003e  \n```20/06/2016, DL, VRNN```  \n```Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron Courville, Yoshua Bengio, NIPS 2015```   \n* \u003ca href=\"http://arxiv.org/abs/1406.5298\"\u003eSemi-Supervised Learning with Deep Generative Models\u003c/a\u003e  \n```20/06/2016, DL, generative```  \n```Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed, Max Welling, NIPS 2014```   \n* \u003ca href=\"http://home.uchicago.edu/~arij/journalclub/papers/2015_Mnih_et_al.pdf\"\u003eHuman-level control through deep reinforcement learning\u003c/a\u003e  \n```19/06/2016, RL, AI, DL```  \n```Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Nature```   \n* \u003ca href=\"http://arxiv.org/abs/1506.02557\"\u003eVariational Dropout and the Local Reparameterization Trick\u003c/a\u003e  \n```19/06/2016, DL, dropout```  \n```Diederik P. Kingma, Tim Salimans, Max Welling, NIPS 2015```   \n* \u003ca href=\"http://arxiv.org/abs/1505.01121\"\u003eAsk Your Neurons: A Neural-based Approach to Answering Questions about Images\u003c/a\u003e  \n```19/06/2016, CV, DL, NLP```  \n```Mateusz Malinowski, Marcus Rohrbach, Mario Fritz, ICCV 2015```   \n* \u003ca href=\"http://arxiv.org/abs/1511.02793\"\u003eGenerating Images from Captions with Attention\u003c/a\u003e  \n```19/06/2016, CV, DL```  \n```Elman Mansimov, Emilio Parisotto, Jimmy Lei Ba, Ruslan Salakhutdinov, ICLR 2016```   \n* \u003ca href=\"http://arxiv.org/pdf/1503.04069.pdf\"\u003eLSTM: A Search Space Odyssey\u003c/a\u003e  \n```19/06/2016, DL, NLP```  \n```Klaus Greff, Rupesh Kumar Srivastava, Jan Koutnik, Bas R. Steunebrink, Jurgen Schmidhuber, arXiv```   \n* \u003ca href=\"https://arxiv.org/abs/1606.03657\"\u003eInfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets\u003c/a\u003e  \n```18/06/2016, DL, InfoGAN```  \n```Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel, arXiv```   \n* \u003ca href=\"http://arxiv.org/abs/1606.03498\"\u003eImproved Techniques for Training GANs\u003c/a\u003e  \n```18/06/2016, DL, GAN```  \n```Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, arXiv```   \n* \u003ca href=\"http://web.eecs.umich.edu/~honglak/icml2016-crelu-full.pdf\"\u003e Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units\u003c/a\u003e  \n```17/06/2016, DL, CV```  \n```Wenling Shang, Kihyuk Sohn, Diogo Almeida, and Honglak Lee, ICML 2016```   \n* \u003ca href=\"http://nlp.stanford.edu/pubs/sidaw13fast.pdf\"\u003eFast dropout training\u003c/a\u003e  \n```17/06/2016, DL, dropout```  \n```Sida I. Wang, Christopher D. Manning, ICML 2013```   \n* \u003ca href=\"https://www.semanticscholar.org/paper/Stating-the-Obvious-Extracting-Visual-Common-Sense-Yatskar-Ordonez/0dcc768631d9ede8a3679e980b37204b782781b2/pdf\"\u003eStating the Obvious: Extracting Visual Common Sense Knowledge\u003c/a\u003e  \n```15/06/2016, DL, NLP```  \n```Mark Yatskar, Vicente Ordonez, Ali Farhadi, NAACL 2016```   \n* \u003ca href=\"https://arxiv.org/abs/1605.06676\"\u003eLearning to Communicate with Deep Multi-Agent Reinforcement Learning\u003c/a\u003e  \n```15/06/2016, DL, RL```  \n```Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, Shimon Whiteson, arXiv```   \n* \u003ca href=\"https://intelligence.org/files/Interruptibility.pdf\"\u003eSafely Interruptible Agents\u003c/a\u003e  \n```15/06/2016, AI, RL, safety```  \n```Laurent Orseau, Stuart Armstrong, UAI 2016```   \n* \u003ca href=\"http://arxiv.org/pdf/1509.06113.pdf\"\u003eDeep Spatial Autoencoders for Visuomotor Learning\u003c/a\u003e  \n```15/06/2016, CV, DL, RL, robotics```  \n```Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, Pieter Abbeeel, ICRA 2016```   \n* \u003ca href=\"https://arxiv.org/pdf/1604.00676.pdf\"\u003eMulti-Bias Non-linear Activation in Deep Neural Networks\u003c/a\u003e  \n```15/06/2016, ML, DL, activation function```  \n```Hongyang Li, Wanli Ouyang, Xiaogang Wang, ICML 2016```   \n* \u003ca href=\"http://arxiv.org/pdf/1511.07275.pdf\"\u003eLearning Simple Algorithms from Examples\u003c/a\u003e  \n```15/06/2016, ML, DL, AI```  \n```Wojciech Zaremba, Tomas Mikolov, Armand Joulin, Rob Fergus, ICML 2016```   \n* \u003ca href=\"http://www.iro.umontreal.ca/~vincentp/Publications/denoising_autoencoders_tr1316.pdf\"\u003eExtracting and Composing Robust Features with Denoising Autoencoders\u003c/a\u003e  \n```15/06/2016, DL, DAE```  \n```Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pierre-Antoine Manzagol, ICML 2008```   \n* \u003ca href=\"http://arxiv.org/abs/1602.07019\"\u003eSentence Similarity Learning by Lexical Decomposition and Composition\u003c/a\u003e  \n```14/06/2016, DL, NLP```  \n```Zhiguo Wang, Haitao Mi, Abraham Ittycheriah, arXiv```   \n* \u003ca href=\"http://arxiv.org/abs/1511.06811\"\u003eLearning visual groups from co-occurrences in space and time\u003c/a\u003e  \n```14/06/2016, CV, DL```  \n```Phillip Isola, Daniel Zoran, Dilip Krishnan, Edward H. Adelson, ICLR 2016```   \n* \u003ca href=\"https://arxiv.org/abs/1605.08104\"\u003eDeep Predictive Coding Networks for Video Prediction and Unsupervised Learning\u003c/a\u003e  \n```14/06/2016, CV, DL```  \n```William Lotter, Gabriel Kreiman, David Cox, arxiv```   \n* \u003ca href=\"http://arxiv.org/abs/1606.04080\"\u003eMatching Networks for One Shot Learning\u003c/a\u003e  \n```14/06/2016, DL, one-shot```  \n```Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra, arxiv```   \n* \u003ca href=\"https://arxiv.org/abs/1512.07679\"\u003eDeep Reinforcement Learning in Large Discrete Action Spaces\u003c/a\u003e  \n```14/06/2016, DL, RL```  \n```Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt, Peter Sunehag, Timothy Lillicrap, Jonathan Hunt, Timothy Mann, Theophane Weber, Thomas Degris, Ben Coppin, arxiv```   \n* \u003ca href=\"http://arxiv.org/abs/1603.06277\"\u003eComposing graphical models with neural networks for structured representations and fast inference\u003c/a\u003e  \n```14/06/2016, DL, graphical-models```  \n```Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko, Sandeep R. Datta, Ryan P. Adams, arXiv```   \n* \u003ca href=\"http://arxiv.org/pdf/1506.06726.pdf\"\u003eSkip-Thought Vectors\u003c/a\u003e  \n```13/06/2016, DL, NLP```  \n```Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard Zemel, Antonio Torralba, Raquel Urtasun, Sanja Fidler, NIPS 2015```   \n* \u003ca href=\"http://arxiv.org/pdf/1512.08512.pdf\"\u003eVisually Indicated Sounds\u003c/a\u003e  \n```12/06/2016, CV, DL```  \n```Andrew Owens, Philip Isola, Josh McDermott, Antonio Torralba, Edward Adelson, William Freeman, CVPR 2016```   \n* \u003ca href=\"https://arxiv.org/pdf/1502.04623.pdf\"\u003eDRAW: A Recurrent Neural Network for Image Generation\u003c/a\u003e  \n```11/06/2016, CV, DL, DRAW```  \n```Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra, JMLR 2015```   \n* \u003ca href=\"http://arxiv.org/pdf/1511.07838.pdf\"\u003eDynamic Capacity Networks\u003c/a\u003e  \n```10/06/2016, DL```  \n```Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, Aaron Courville, JMLR, 2016```   \n* \u003ca href=\"https://arxiv.org/pdf/1412.7210.pdf\"\u003eDenoising Autoencoder with Modulated Lateral Connections learns Invariant Representations of Natural Images\u003c/a\u003e  \n```10/06/2016, CV, DL, ladder-networks```  \n```Antii Rasmus, Tapani Raiko, Harri Valpola, ICLR 2015```   \n* \u003ca href=\"https://arxiv.org/pdf/1511.06434.pdf\"\u003eUnsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks\u003c/a\u003e  \n```10/06/2016, CV, DL, DCGAN```  \n```Alec Radford, Luke Metz, Soumith Chintala, ICLR 2016```   \n* \u003ca href=\"https://arxiv.org/pdf/1604.03357.pdf\"\u003eImproving sentence compression by learning to predict gaze\u003c/a\u003e  \n```09/06/2016, DL, NLP```  \n```Sigrid Klerke, Yoav Goldberg, Anders Sogaard, NAACL 2016, Best Short Paper```   \n* \u003ca href=\"http://arxiv.org/pdf/1604.04378.pdf\"\u003eMatch-SRNN: Modeling the Recursive Matching Structure with Spatial RNN\u003c/a\u003e  \n```08/06/2016, DL, NLP```  \n```Shengxian Wan, Yanyan Lan, Jun Xu, Jiafeng Guo, IJCAI 2016```   \n* \u003ca href=\"http://arxiv.org/abs/1506.05751\"\u003eDeep Generative Image Models using a Laplacian Pyramid of Adversarial Networks\u003c/a\u003e  \n```08/06/2016, DL, CV, GAN, LAPGAN```  \n```Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus, NIPS 2015```   \n* \u003ca href=\"https://arxiv.org/pdf/1511.02799.pdf\"\u003eNeural Module Networks\u003c/a\u003e  \n```08/06/2016, DL, CV, visual QA```  \n```Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein, arXiv```   \n* \u003ca href=\"http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf\"\u003eA Neural Probabilistic Language Model\u003c/a\u003e  \n```07/06/2016, DL, NLP```  \n```Yoshua Bengio, Rejean Ducharme, Pascal Vincent, Christian Jauvin, JMLR 2003```   \n* \u003ca href=\"https://arxiv.org/pdf/1606.00704.pdf\"\u003eAdversarially Learned Inference\u003c/a\u003e  \n```07/06/2016, ML, DL, inference, generative model```  \n```Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martin Arjovsky, Olivier Mastropietro, Aaron Courville, Subt. NIPS 2016```   \n* \u003ca href=\"https://arxiv.org/pdf/1606.01885v1.pdf\"\u003eLearning to Optimize\u003c/a\u003e  \n```06/06/2016, DL, optimization```  \n```Ke Li, Jitendar Malik, arxiv```   \n* \u003ca href=\"https://arxiv.org/abs/1312.6114\"\u003eAuto-Encoding Variational Bayes\u003c/a\u003e  \n```06/06/2016, VAE```  \n```Diederik P Kingma, Max Welling, ICLR 2014```   \n* \u003ca href=\"http://arxiv.org/abs/1506.08941\"\u003eLanguage Understanding for Text-based Games Using Deep Reinforcement Learning\u003c/a\u003e  \n```06/06/2016, DL, NLP, RL```  \n```Karthik Narasimhan, Tejas Kulkarni, Regina Barzilay, EMNLP 2015```   \n* \u003ca href=\"http://arxiv.org/pdf/1411.4166.pdf\"\u003eRetrofitting Word Vectors to Semantic Lexicons\u003c/a\u003e  \n```06/06/2016, NLP, word vectors```  \n```Manaal Faruqui, Jesse Dodge, Sujay K. Jauhar, Chris Dyer, Eduard Hovy, Noah A. Smith, NAACL 2015```   \n* \u003ca href=\"http://www.jmlr.org/papers/volume13/gutmann12a/gutmann12a.pdf\"\u003eNoise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics\u003c/a\u003e  \n```06/06/2016, ML, non-parametric estimation```  \n```Michael U. Gutmann, Aapo Hyvarinen, JMLR 2012```   \n* \u003ca href=\"http://www.cs.toronto.edu/~fritz/absps/nccd.pdf\"\u003eTraining Products of Experts by Minimizing Contrastive Divergence\u003c/a\u003e  \n```06/06/2016, ML, contrastive divergence```  \n```Geoffrey E. Hinton, Neural Computation 2002```   \n* \u003ca href=\"https://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization.pdf\"\u003eNeural Word Embedding as Implicit Matrix Factorization\u003c/a\u003e  \n```06/06/2016, DL, NLP, word vectors```  \n```Omer Levy, Yoav Goldberg, NIPS 2014```   \n* \u003ca href=\"https://arxiv.org/pdf/1604.07379.pdf\"\u003eContext Encoders: Feature Learning by Inpainting\u003c/a\u003e  \n```05/06/2016, CV, DL, context-encoder```  \n```Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, Alexei AEfros, CVPR 2016```   \n* \u003ca href=\"http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf\"\u003eLong Short-term Memory\u003c/a\u003e  \n```04/06/2016, DL, RNN, LSTM```  \n```Sepp Hochreiter, Jurgen Schmidhuber, Neural Computation, 1997```   \n* \u003ca href=\"https://arxiv.org/pdf/1302.4389.pdf\"\u003eMaxout Networks\u003c/a\u003e  \n```03/06/2016, DL, dropout, maxout```  \n```Ian Goodfellow, David Farley, Mehdi Mirza, Aaron Courville, Yoshua Bengio, JMLR 2013```   \n* \u003ca href=\"http://arxiv.org/pdf/1402.3511.pdf\"\u003eA Clockwork RNN\u003c/a\u003e  \n```02/06/2016, DL, RNN, clock-work```  \n```Jan Koutnik, Klaus Greff, Faustino Gomez, Jurgen Schmidhuber, JMLR 2014```   \n* \u003ca href=\"http://arxiv.org/pdf/1601.06733.pdf\"\u003eLong Short-Term Memory-Networks for Machine Reading\u003c/a\u003e  \n```02/06/2016, DL, NLP, machine understanding```  \n```Jianpeng Cheng, Li Dong, Mirella Lapata, ```   \n* \u003ca href=\"http://nlp.stanford.edu/pubs/emnlp15_attn.pdf\"\u003eEffective Approaches to Attention-based Neural Machine Translation\u003c/a\u003e  \n```02/06/2016, DL, NLP, neural machine translation```  \n```Minh-Thang Luong, Hieu Pham, Christopher D. Manning, EMNLP 2015```   \n* \u003ca href=\"http://www.aclweb.org/anthology/D15-1044\"\u003eA Neural Attention Model for Sentence Summarization\u003c/a\u003e  \n```02/06/2016, DL, NLP, summarization```  \n```Alexander M. Rush, Sumit Chopra, Jason Weston, EMNLP 2015```   \n* \u003ca href=\"http://www-devel.cs.ubc.ca/~tmm/courses/cpsc533c-04-spr/morereadings/PsychSci97-RR.pdf\"\u003eTo See or not to See : The need for attention to perrceive changes in scenes\u003c/a\u003e  \n```01/06/2016, attention, vision```  \n```Ronald Rensink, Kevin O'Regan, James Clark, Psychological Science, 1997```   \n* \u003ca href=\"http://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf\"\u003eTeaching Machines to Read and Comprehend\u003c/a\u003e  \n```01/06/2016, DL, NLP, attention```  \n```Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom, NIPS 2015```   \n* \u003ca href=\"https://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention.pdf\"\u003eRecurrent Models of Visual Attention\u003c/a\u003e  \n```01/06/2016, CV, DL, RL, attention```  \n```Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu, NIPS 2014```   \n* \u003ca href=\"https://arxiv.org/abs/1601.01705\"\u003eLearning to compose neural networks for question answering\u003c/a\u003e  \n```01/06/2016, DL, compose-NN, RL, QA```  \n```Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein, NAACL 2016 (best-paper)```   \n* \u003ca href=\"https://arxiv.org/abs/1602.01783\"\u003eAsynchronous Methods for Deep Reinforcement Learning\u003c/a\u003e  \n```01/06/2016, DL, RL```  \n```Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu, 2016```   \n* \u003ca href=\"http://arxiv.org/pdf/1605.08803.pdf\"\u003eDensity estimation using Real NVP\u003c/a\u003e  \n```01/06/2016, DL, latent space, image generation```  \n```Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio, Google Brain```   \n* \u003ca href=\"https://papers.nips.cc/paper/4334-sparse-filtering.pdf\"\u003eSparse Filtering\u003c/a\u003e  \n```01/06/2016, ML, DL, sparse```  \n```Jiquan Ngiam, Pang Wei Koh, Zhenghao Chen, Sonia Bhaskar, Andrew Y. Ng, NIPS 2011```   \n* \u003ca href=\"http://arxiv.org/pdf/1505.00521.pdf\"\u003eReinforcement Learning Neural Turing Machines\u003c/a\u003e  \n```31/05/2016, DL, NTM, RL```  \n```Wojciech Zaremba, ICLR 2016```   \n* \u003ca href=\"https://arxiv.org/abs/1510.03009\"\u003eNeural Networks with Few Multiplications\u003c/a\u003e  \n```31/05/2016, DL, optimization```  \n```Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, Yoshua Bengio, ICLR 2016```   \n* \u003ca href=\"http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf\"\u003eRecurrent neural network based language model\u003c/a\u003e  \n```30/05/2016, DL, NLP, RNN, language-model```  \n```Tomas Mikolov, Martin Karafiat, Lukas Burget, Jan Cernocky, Sanjeev Khudanpur, INTERSPEECH 2010```   \n* \u003ca href=\"http://arxiv.org/abs/1506.07285\"\u003eAsk Me Anything: Dynamic Memory Networks for Natural Language Processing\u003c/a\u003e  \n```30/05/2016, DL, DMN, NLP, dynamic-memory-networks```  \n```Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, Richard Socher, ICML 2016```   \n* \u003ca href=\"http://arxiv.org/pdf/1511.06342.pdf\"\u003eActor-Mimic : Deep Multitask and Transfer Reinforcement Learning\u003c/a\u003e  \n```30/05/2016, DL, RL, actor-mimic```  \n```Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov, ICLR 2016```   \n* \u003ca href=\"http://arxiv.org/abs/1511.06279\"\u003eNeural Programmer-Interpreters\u003c/a\u003e  \n```29/05/2016, DL, NPI```  \n```Scott Reed, Nando de Freitas, ICLR 2016```   \n* \u003ca href=\"http://arxiv.org/abs/1504.00702\"\u003eEnd-to-End Training of Deep Visuomotor Policies\u003c/a\u003e  \n```29/05/2016, CV, DL, RL, robotics, control```  \n```Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel, JMLR 2016```   \n* \u003ca href=\"http://arxiv.org/abs/1512.02902\"\u003eMovieQA : Understanding Stories in Movies through Question-Answering\u003c/a\u003e  \n```28/05/2016, CV, DL, QA, movie-story```  \n```Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urtasun, Sanja Fidler, CVPR 2016```   \n* \u003ca href=\"http://arxiv.org/pdf/1511.06361.pdf\"\u003eOrder-Embeddings of Images and Language\u003c/a\u003e  \n```28/05/2016, CV, DL, image-caption, hierarchy```  \n```Ivan Vendrov, Ryan Kiros, Sanja Fidler, Raquel Urtasun, ICLR 2016```   \n* \u003ca href=\"http://arxiv.org/pdf/1511.04119.pdf\"\u003eAction Recognition using Visual Attention\u003c/a\u003e  \n```28/05/2016, CV, DL, action-recognition, attention```  \n```Shikhar Sharma, Ryan Kiros, Ruslan Salakhutdinov, ICLR 2016```   \n* \u003ca href=\"http://arxiv.org/abs/1503.08895\"\u003eEnd-To-End Memory Networks\u003c/a\u003e  \n```28/05/2016, DL, memory-networks, end-to-end```  \n```Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus, NIPS 2015```   \n* \u003ca href=\"http://arxiv.org/abs/1511.06581\"\u003eDueling Network Architectures for Deep Reinforcement Learning\u003c/a\u003e  \n```27/05/2016, DL, dueling-networks, RL```  \n```Ziyu Whang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas, ICML 2016```   \n* \u003ca href=\"http://arxiv.org/pdf/1410.39161.pdf\"\u003eMemory Networks\u003c/a\u003e  \n```27/05/2016, DL, memory-networks```  \n```Jason Weston, Sumit Chopra, Antoine Bordes, ICLR 2015```   \n* \u003ca href=\"http://arxiv.org/abs/1312.5602\"\u003ePlaying Atari with Deep Reinforcement Learning\u003c/a\u003e  \n```27/05/2016, DL, DQN, RL```  \n```Volodymyr Mnih et al, NIPS DL workshop 2013```   \n* \u003ca href=\"https://arxiv.org/pdf/1603.09382.pdf\"\u003eDeep Networks with Stochastic Depth\u003c/a\u003e  \n```27/05/2016, DL, stochastic-depth```  \n```Gao Huang, Yu Sun et al, 2016```   \n* \u003ca href=\"http://people.eecs.berkeley.edu/~jordan/papers/variational-intro.pdf\"\u003eAn Introduction to Variational Methods for Graphical Models\u003c/a\u003e  \n```26/05/2016, graphical-models, ML, variational-methods```  \n```Michael Jordan et al, Machine Learning 1999```   \n* \u003ca href=\"http://arxiv.org/pdf/1412.2306.pdf\"\u003eDeep Visual-Semantic Alignments for Generating Image Descriptions\u003c/a\u003e  \n```26/05/2016, CV, DL, image-captioning, NLP```  \n```Andrej Karpathy, Li Fei-Fei, CVPR 2015```   \n* \u003ca href=\"https://arxiv.org/pdf/1505.00468.pdf\"\u003eVQA : Visual Question Answering\u003c/a\u003e  \n```26/05/2016, CV, DL, QA```  \n```Aishwarya Agarwal et al, ICCV 2015```   \n* \u003ca href=\"https://papers.nips.cc/paper/5956-scheduled-sampling-for-sequence-prediction-with-recurrent-neural-networks.pdf\"\u003eScheduled Sampling for Sequence Prediction with Recurrent Neural Networks\u003c/a\u003e  \n```26/05/2016, DL, RNN, scheduled-sampling```  \n```Samy Bengio et al, NIPS 2015```   \n* \u003ca href=\"http://arxiv.org/pdf/1502.03167.pdf\"\u003eBatch Normalization : Accelerating Deep Network Training by Reducing Covariate Shift\u003c/a\u003e  \n```26/05/2016, batch-norm, DL```  \n```Sergey Ioffe, Christian Szegedy, JMLR 2015```   \n* \u003ca href=\"http://arxiv.org/abs/1502.03044\"\u003eShow, Attend and Tell: Neural Image Caption Generation with Visual Attention\u003c/a\u003e  \n```25/05/2016, CV, DL, attention, caption```  \n```Kelvin Xu et al, JMLR 2015```   \n* \u003ca href=\"https://papers.nips.cc/paper/5866-pointer-networks.pdf\"\u003ePointer Networks\u003c/a\u003e  \n```24/05/2016, DL, Pointer-Nets```  \n```Oriol Vinyals, Meire Fortunato, Navdeep Jaitly, NIPS 2015```   \n* \u003ca href=\"http://arxiv.org/pdf/1511.06391.pdf\"\u003eOrder Matters : Sequence to Sequence for sets\u003c/a\u003e  \n```24/05/2016, DL, seq2seq, ordered, sorting```  \n```Oriol Vinyals, Samy Bengio, Manjunath Kudlur, ICLR 2016```   \n* \u003ca href=\"http://arxiv.org/pdf/1409.0473.pdf\"\u003eNeural Machine Translation by Jointly Learning to Align and Translate\u003c/a\u003e  \n```23/05/2016, DL, NMT```  \n```Bahdanau, Cho, Bengio, ICLR 2015```   \n* \u003ca href=\"http://www.cs.toronto.edu/~hinton/absps/tsne.pdf\"\u003eVisualizing Data using t-SNE\u003c/a\u003e  \n```23/05/2016, ML, Embeddings```  \n```Maaten, Hinton,, JMLR 2008```   \n* \u003ca href=\"http://arxiv.org/pdf/1406.1078.pdf\"\u003eLearning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation\u003c/a\u003e  \n```22/05/2016, DL, NLP, RNN-ED```  \n```Kyunghyun Cho et al, ACL 2014```   \n* \u003ca href=\"https://arxiv.org/pdf/1512.03385.pdf\"\u003eDeep Residual Learning for Image Recognition\u003c/a\u003e  \n```22/05/2016, CV, DL, ResNets```  \n```Kaiming He et al, 2015```   \n* \u003ca href=\"https://arxiv.org/pdf/1410.5401.pdf\"\u003eNeural Turing Machines\u003c/a\u003e  \n```22/05/2016, DL, NTM```  \n```Alex Graves et al, 2014```   \n* \u003ca href=\"http://machinelearning.org/proceedings/icml2004/papers/76.pdf\"\u003eSupport Vector Machine Learning for Interdependent and Structured Output Spaces\u003c/a\u003e  \n```21/05/2016, ML, StructSVM```  \n```Ioannis Tsochantaridis et al, ICML 2004```   \n* \u003ca href=\"http://arxiv.org/abs/1406.2661\"\u003eGenerative Adversarial Networks\u003c/a\u003e  \n```21/05/2016, DL, GAN, generative```  \n```Ian Goodfellow et al, NIPS 2014```   \n* \u003ca href=\"http://arxiv.org/pdf/1409.3215.pdf\"\u003eSequence to Sequence learning with neural networks\u003c/a\u003e  \n```21/05/2016, DL, Seq2Seq```  \n```Ilya Sutskever, Oriol Vinyals, and Quoc Le, NIPS 2014```   \n* \u003ca href=\"https://arxiv.org/pdf/1412.6980.pdf\"\u003eAdam : A Method for Stochastic Optimization\u003c/a\u003e  \n```20/05/2016, ML, Optimization, ADAM```  \n```Diederik Kingma, Jimmy Ba, ICLR 2015```   \n* \u003ca href=\"http://arxiv.org/pdf/1511.08228.pdf\"\u003eNeural GPUs Learn Algorithms\u003c/a\u003e  \n```20/05/2016, DL```  \n```Lukasz Kaiser, Ilya Sutskever, ICLR 2016```   \n* \u003ca href=\"http://arxiv.org/pdf/1308.0850.pdf\"\u003eGenerating Sequences With Recurrent Neural Networks\u003c/a\u003e  \n```19/05/2016, DL```  \n```Alex Graves, 2014```   \n* \u003ca href=\"http://arxiv.org/pdf/1605.05396.pdf\"\u003eGenerative Adversarial Text to Image Synthesis\u003c/a\u003e  \n```19/05/2016, CV, DL```  \n```Scott Reed et al, ICML 2016```   \n* \u003ca href=\"https://papers.nips.cc/paper/5165-learning-word-embeddings-efficiently-with-noise-contrastive-estimation.pdf\"\u003eLearning word embeddings efficiently with noise-contrastive estimation\u003c/a\u003e  \n```18/05/2016, DL, NLP```  \n```Andriy Mnih et al, NIPS 2013```   \n* \u003ca href=\"http://arxiv.org/pdf/1511.06349.pdf\"\u003eGenerating Sentences from a Continuous Space\u003c/a\u003e  \n```18/05/2016, DL, NLP```  \n```Samuel Bowman et al, 2015```   \n* \u003ca href=\"https://www.jair.org/media/301/live-301-1562-jair.pdf\"\u003eReinforcement Learning: A Survey\u003c/a\u003e  \n```18/05/2016, AI, ML, RL```  \n```Leslie Kaebling et al, JAIR 1996```   \n* \u003ca href=\"http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf\"\u003eSimple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning\u003c/a\u003e  \n```18/05/2016, AI, ML, REINFORCE```  \n```Ronald Williams, Machine Learning 1992```   \n* \u003ca href=\"http://arxiv.org/pdf/1605.02026.pdf\"\u003eTraining Neural Networks Without Gradients: A Scalable ADMM Approach\u003c/a\u003e  \n```17/05/2016, DL```  \n```Gavin Taylor et al, ICML 2016```   \n* \u003ca href=\"http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf\"\u003ek-means++: The Advantages of Careful Seeding\u003c/a\u003e  \n```17/05/2016, Clustering, ML```  \n```David Arthur et al, SODA 2007```   \n* \u003ca href=\"https://arxiv.org/abs/1604.03640\"\u003eBridging the Gaps Betweeen Residual Learning, Recurrent Neural Networks and Visual Cortex\u003c/a\u003e  \n```13/04/2016, CV, DL, cortex```  \n```Quanli Liao, Tomas Poggio, arxiv```   \n* \u003ca href=\"http://www.utexas.edu/cola/files/1515661\"\u003eEye movements in natural behaviour\u003c/a\u003e  \n```01/06/2015, eye-movement, attention```  \n```Mary Hayhoe, Dana Ballard, Trends in Cognitive Sciences, 2005```   \n* \u003ca href=\"https://arxiv.org/abs/1503.05671\"\u003eOptimizing Neural Networks with Kronecker-factored Approximate Curvature\u003c/a\u003e  \n```19/03/2015, K-FAC```  \n```James Martens, Roger Grosse, ICML 2015```   \n* \u003ca href=\"http://arxiv.org/pdf/1402.0119v2.pdf\"\u003eRandomized Nonlinear Component Analysis\u003c/a\u003e  \n```13/05/2014, RNCA, ML```  \n```David Lopez-Paz, Suvrit Sra, Alex Smola, Zoubin Grahramani, Bernhard Scholkopf, ICML, 2014```   \n* \u003ca href=\"http://www.jstor.org/stable/2236703?seq=1#page_scan_tab_contents\"\u003eOn Information and Sufficiency\u003c/a\u003e  \n```01/03/1951, classics, KL-divergence, information theory```  \n```S. Kullback and R. A. Leibler, The Annals of Mathematical Statistics``` \u003ca href=\"https://kumarkrishna.github.io/pages/classics/on-information-and-sufficiency.html\"\u003e\\[Review\\]\u003c/a\u003e  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkumarkrishna%2Fpaper-spray","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkumarkrishna%2Fpaper-spray","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkumarkrishna%2Fpaper-spray/lists"}