ICLR 2017深度学习(提交)论文汇总:NLP、无监督学习、自动编码、RL、RNN(150论文下载)

【新智元导读】ICLR 2017 将于2017年4月24日至26日在法国土伦(toulon)举行,11月4日已经停止接收论文。本文汇总了本年度NLP、无监督学习、对抗式生成、自动编码、增强学习、随机循环梯度渐变、RNN等多个领域的150篇论文。其中不乏Yoshua Bengio、Ian Goodfellow、Yann LeCun、李飞飞、邓力等学者的作品。从收录的论文主题来看,生成和对抗生成式网络的研究成为热点,一共有45篇论文被提交,数量排在第一。文内附下载。

ICLR 2017 将于2017年4月24日至26日在法国土伦举行,向大会提交的深度学习论文非常多,无疑这将成为一场盛会(下图展示了提交的论文题目中最频繁出现的单词),可以看到,深度、学习、递归、模型、网络、表征、对抗式、生成等成为热词。

与ICLR 2016 相比有哪些变化?

将使用 OpenReview(而不是 CMT)作为会议通道。此外,提交的论文将交由 OpenReview 管理(无需提交到 arXiv)。

审查程序将变成两轮。第一轮中,审稿人只能提出澄清性的疑问。程序委员会将评出最佳审稿奖,得奖的审稿人将被列入 ICLR 2018 的候选人名单中。研讨会通道鼓励那些具有高度创新性,但可能未得到充分验证的提交论文。

评审委员会说,采用 OpenReview 的目标是提高整体审稿过程的质量。OpenReview 可以让作者随时对论文的评论进行回复。此外,社区中的任何人都可以对提交的论文进行评论,审稿者可以利用公开讨论来提高他们对论文的理解和评级。

下文是对提交给 ICLR 2017 的论文中与自然语言处理(NLP)相关的论文的概览,由前 Google 工程师、ZEDGE数据副总裁,AI 顾问/投资者, Memkite 和 Atbrox 的创始人/联合创始人Amund Tveit整理。

ICLR 2017 – NLP 论文

在新智元微信公众号回复1113,下载全部37篇论文。

1.字符/词/句子表征

  1. Character-aware Attention Residual Network for Sentence Representation 作者: Xin Zheng, Zhenzhou Wu
  2. Program Synthesis for Character Level Language Modeling 作者: Pavol Bielik, Veselin Raychev, Martin Vechev
  3. Words or Characters? Fine-grained Gating for Reading Comprehension 作者: Zhilin Yang, Bhuwan Dhingra, Ye Yuan, Junjie Hu, William W. Cohen, Ruslan Salakhutdinov
  4. Deep Character-Level Neural Machine Translation By Learning Morphology 作者: Shenjian Zhao, Zhihua Zhang
  5. Opening the vocabulary of neural language models with character-level word representations 作者: Matthieu Labeau, Alexandre Allauzen
  6. Unsupervised sentence representation learning with adversarial auto-encoder 作者: Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang
  7. Offline Bilingual Word Vectors Without a Dictionary 作者: Samuel L. Smith, David H. P. Turban, Nils Y. Hammerla, Steven Hamblin
  8. Learning Word-Like Units from Joint Audio-Visual Analylsis 作者:David Harwath, James R. Glass
  9. Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling 作者: Hakan Inan, Khashayar Khosravi, Richard Socher
  10. Sentence Ordering using Recurrent Neural Networks 作者: Lajanugen Logeswaran, Honglak Lee, Dragomir Radev

2. 搜索/问答/推荐系统

  1. Learning to Query, Reason, and Answer Questions On Ambiguous Texts 作者: Xiaoxiao Guo, Tim Klinger, Clemens Rosenbaum, Joseph P. Bigus, Murray Campbell, Ban Kawas, Kartik Talamadupula, Gerry Tesauro, Satinder Singh
  2. Group Sparse CNNs for Question Sentence Classification with Answer Sets 作者: Mingbo Ma, Liang Huang, Bing Xiang, Bowen Zhou
  3. CONTENT2VEC: Specializing Joint Representations of Product Images and Text for the task of Product Recommendation 作者: Thomas Nedelec, Elena Smirnova, Flavian Vasile
  4. Is a picture worth a thousand words? A Deep Multi-Modal Fusion Architecture for Product Classification in e-commerce 作者: Tom Zahavy, Alessandro Magnani, Abhinandan Krishnan, Shie Mannor

3.词/句嵌入

  1. A Simple but Tough-to-Beat Baseline for Sentence Embeddings 作者: Sanjeev Arora, Yingyu Liang, Tengyu Ma
  2. Investigating Different Context Types and Representations for Learning Word Embeddings
  3. 作者: Bofang Li, Tao Liu, Zhe Zhao, Xiaoyong Du
  4. Multi-view Recurrent Neural Acoustic Word Embeddings 作者: Wanjia He, Weiran Wang, Karen Livescu
  5. A Self-Attentive Sentence Embedding 作者: Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, Yoshua Bengio

(推荐关注)

5. Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks

作者: Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, Yoav Goldberg

4.多语言/翻译/情感

  1. Neural Machine Translation with Latent Semantic of Image and Text 作者: Joji Toyama, Masanori Misono, Masahiro Suzuki, Kotaro Nakayama, Yutaka Matsuo
  2. Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context 作者: Shyam Upadhyay, Kai-Wei Chang, James Zhou, Matt Taddy, Adam Kalai
  3. Learning to Understand: Incorporating Local Contexts with Global Attention for Sentiment Classification 作者: Zhigang Yuan, Yuting Hu, Yongfeng Huang
  4. Adaptive Feature Abstraction for Translating Video to Language 作者: Yunchen Pu, Martin Renqiang Min, Zhe Gan, Lawrence Carin
  5. A Convolutional Encoder Model for Neural Machine Translation 作者: Jonas Gehring, Michael Auli, David Grangier, Yann N. Dauphin
  6. Fuzzy paraphrases in learning word representations with a corpus and a lexicon 作者: Yuanzhi Ke, Masafumi Hagiwara
  7. Iterative Refinement for Machine Translation 作者: Roman Novak, Michael Auli, David Grangier
  8. Vocabulary Selection Strategies for Neural Machine Translation 作者: Gurvan L’Hostis, David Grangier, Michael Auli

5.语言模型/文本理解/配对/压缩/分类/++

  1. A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks 作者: Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, Richard Socher
  2. Gated-Attention Readers for Text Comprehension 作者: Bhuwan Dhingra, Hanxiao Liu, Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov
  3. A Compare-Aggregate Model for Matching Text Sequences 作者: Shuohang Wang, Jing Jiang
  4. A Context-aware Attention Network for Interactive Question Answering 作者: Huayu Li, Martin Renqiang Min, Yong Ge, Asim Kadav
  5. FastText.zip: Compressing text classification models 作者: Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Herve Jegou, Tomas Mikolov
  6. Multi-Agent Cooperation and the Emergence of (Natural) Language 作者: Angeliki Lazaridou, Alexander Peysakhovich, Marco Baroni
  7. Learning a Natural Language Interface with Neural Programmer 作者: Arvind Neelakantan, Quoc V. Le, Martin Abadi, Andrew McCallum, Dario Amodei
  8. Learning similarity preserving representations with neural similarity and context encoders 作者: Franziska Horn, Klaus-Robert Müller
  9. Adversarial Training Methods for Semi-Supervised Text Classification 作者: Takeru Miyato, Andrew M. Dai, Ian Goodfellow (推荐关注)
  10. Multi-Label Learning using Tensor Decomposition for Large Text Corpora 作者: Sayantan Dasgupta

以下论文均可在https://amundtveit.com/直接下载

ICLR 2017 —无监督深度学习论文

  1. Unsupervised Learning Using Generative Adversarial Training And Clustering – 作者: Vittal Premachandran, Alan L. Yuille
  2. An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax 作者: Wentao Huang, Kechen Zhang
  3. Unsupervised Cross-Domain Image Generation 作者: Yaniv Taigman, Adam Polyak, Lior Wolf
  4. Unsupervised Perceptual Rewards for Imitation Learning 作者: Pierre Sermanet, Kelvin Xu, Sergey Levine
  5. Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning 作者: William Lotter, Gabriel Kreiman, David Cox
  6. Unsupervised sentence representation learning with adversarial auto-encoder – 作者: Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang
  7. Unsupervised Program Induction with Hierarchical Generative Convolutional Neural Networks
  8. 作者: Qucheng Gong, Yuandong Tian, C. Lawrence Zitnick
  9. Generalizable Features From Unsupervised Learning 作者: Mehdi Mirza, Aaron Courville, Yoshua Bengio

(推荐关注)

10. Reinforcement Learning with Unsupervised Auxiliary Tasks

作者: Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, Koray Kavukcuoglu

11. Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data

作者: Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der Smagt

12. Unsupervised Learning of State Representations for Multiple Tasks

作者: Antonin Raffin, Sebastian Höfer, Rico Jonschkowski, Oliver Brock, Freek Stulp

13. Unsupervised Pretraining for Sequence to Sequence Learning

作者: Prajit Ramachandran, Peter J. Liu, Quoc V. Le

14. Unsupervised Deep Learning of State Representation Using Robotic Priors

作者: Timothee LESORT, David FILLIAT

15. Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders

作者: Nat Dilokthanakul, Pedro A. M. Mediano, Marta Garnelo, Matthew C.H. Lee, Hugh Salimbeni, Kai Arulkumaran, Murray Shanahan

16. Deep unsupervised learning through spatial contrasting

作者: Elad Hoffer, Itay Hubara, Nir Ailon

ICLR 2017 —自动编码深度学习论文

以下论文均可在https://amundtveit.com/直接下载

  1. Revisiting Denoising Auto-Encoders 作者:Luis Gonzalo Sanchez Giraldo
  2. Epitomic Variational Autoencoders 作者: Serena Yeung, Anitha Kannan, Yann Dauphin, Li Fei-Fei

(推荐关注)

3. Unsupervised sentence representation learning with adversarial auto-encoder

作者: Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang

4. Tree-Structured Variational Autoencoder

作者: Richard Shin, Alexander A. Alemi, Geoffrey Irving, Oriol Vinyals

5. Lossy Image Compression with Compressive Autoencoders

作者: Lucas Theis, Wenzhe Shi, Andrew Cunningham, Ferenc Huszár

6. Variational Lossy Autoencoder

作者: Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel

7. Stick-Breaking Variational Autoencoders

作者: Eric Nalisnick, Padhraic Smyth

8. ParMAC: distributed optimisation of nested functions, with application to binary autoencoders

作者: Miguel A. Carreira-Perpinan, Mehdi Alizadeh

9. Discrete Variational Autoencoders 作者: Jason Tyler Rolfe

10. Deep Unsupervised Clustering with Gaussian Mixture\Variational Autoencoders

作者: Nat Dilokthanakul, Pedro A. M. Mediano, Marta Garnelo, Matthew,C.H. Lee, Hugh Salimbeni, Kai Arulkumaran, Murray Shanahan

11. Improving Sampling from Generative Autoencoders with Markov Chains

作者: Kai Arulkumaran, Antonia Creswell, Anil Anthony Bharath

ICLR 2017 —增强学习深度学习论文

以下论文均可在https://amundtveit.com/直接下载

  1. Stochastic Neural Networks for Hierarchical Reinforcement Learning 作者: Carlos Florensa, Yan Duan, Pieter Abbeel
  2. #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning 作者: Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi C hen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel
  3. Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning 作者: Abhishek Gupta, Coline Devin, YuXuan Liu, Pieter Abbeel, Se rgey Levine
  4. Deep Reinforcement Learning for Accelerating the Convergence Rate 作者: Jie Fu, Zichuan Lin, Danlu Chen, Ritchie Ng, Miao Liu, Nicholas Leonard, Jiashi Feng, Tat-Seng Chua
  5. Generalizing Skills with Semi-Supervised Reinforcement Learning 作者: Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine
  6. Learning to Perform Physics Experiments via Deep Reinforcement Learning – 作者: Misha Denil, Pulkit Agrawal, Tejas D Kulkarni, Tom Erez, Peter Batta glia, Nando de Freitas
  7. Designing Neural Network Architectures using Reinforcement Learning 作者: Bowen Baker, Otkrist Gupta, Nikhil Naik, Ramesh Raskar
  8. Reinforcement Learning with Unsupervised Auxiliary Tasks 作者: Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo,David Silver, Koray Kavukcuoglu
  9. Options Discovery with Budgeted Reinforcement Learning 作者: Aurelia Lon, Ludovic Denoyer
  10. Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU 作者: Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons,Jan Kautz
  11. Multi-task learning with deep model based reinforcement learning 作者:Asier Mujika
  12. Neural Architecture Search with Reinforcement Learning 作者:: Barret Zoph, Quoc Le
  13. Tuning Recurrent Neural Networks with Reinforcement Learning 作者:: Natasha Jaques, Shixiang Gu, Richard E. Turner, Douglas Eck
  14. RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning 作者: Yan Duan, John Schulman, Xi Chen, Peter Bartlett, Ilya Sutskever, Pieter Abbeel
  15. Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning 作者: Sahil Sharma, Aravind S. Lakshminarayanan, Balaraman Ravindran
  16. Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening 作者: Frank S.He, Yang Liu, Alexander G. Schwing, Jian Peng
  17. Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning 作者: Joshua Achiam, Shankar Sastry
  18. Learning to Compose Words into Sentences with Reinforcement Learning 作者:Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, Wang Ling
  19. Spatio-Temporal Abstractions in Reinforcement Learning Through Neural Encoding 作者: Nir Baram, Tom Zahavy, Shie Mannor
  20. Modular Multitask Reinforcement Learning with Policy Sketches 作者:Jacob Andreas, Dan Klein, Sergey Levine
  21. Combating Deep Reinforcement Learning’s Sisyphean Curse with Intrinsic Fear 作者:Zachary C. Lipton, Jianfeng Gao, Lihong Li, Jianshu Chen, Li Deng

(推荐关注)

ICLR 2017 生成和对抗式生成论文(45篇)

以下论文均可在https://amundtveit.com/直接下载

  1. Unsupervised Learning Using Generative Adversarial Training And Clustering 作者: Vittal Premachandran, Alan L. Yuille
  2. Improving Generative Adversarial Networks with Denoising Feature Matching 作者: David Warde-Farley, Yoshua Bengio
  3. Generative Adversarial Parallelization 作者: Daniel Jiwoong Im, He Ma, Chris Dongjoo Kim, Graham Taylor
  4. b-GAN: Unified Framework of Generative Adversarial Networks 作者: Masatosi Uehara, Issei Sato, Masahiro Suzuki, Kotaro Nakayama, Yutaka Matsuo
  5. Generative Adversarial Networks as Variational Training of Energy Based Models 作者:Shuangfei Zhai, Yu Cheng, Rogerio Feris, Zhongfei Zhang
  6. Boosted Generative Models 作者: Aditya Grover, Stefano Ermon
  7. Adversarial examples for generative models 作者: Jernej Kos, Dawn Song
  8. Mode Regularized Generative Adversarial Networks 作者: Tong Che, Yanran Li, Athul Jacob, Yoshua Bengio, Wenjie Li
  9. Variational Recurrent Adversarial Deep Domain Adaptation 作者:: Sanjay Purushotham, Wilka Carvalho, Tanachat Nilanon, Yan Liu
  10. Structured Interpretation of Deep Generative Models 作者: N. Siddharth, Brooks Paige, Alban Desmaison, Jan-Willem van de Meent, Frank Wood, Noah D. Goodman, Pushmeet Kohli, Philip H.S. Torr
  11. Inference and Introspection in Deep Generative Models of Sparse Data 作者:Rahul G. Krishnan, Matthew Hoffman
  12. Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy 作者: Dougal J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alex Smola, Arthur Gretton
  13. Unsupervised sentence representation learning with adversarial auto-encoder 作者: Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang
  14. Unsupervised Program Induction with Hierarchical Generative Convolutional Neural Networks 作者: Qucheng Gong, Yuandong Tian, C. Lawrence Zitnick
  15. A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Noise 作者: Beilun Wang, Ji Gao, Yanjun Qi
  16. On the Quantitative Analysis of Decoder-Based Generative Models 作者: Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, Roger Grosse
  17. Evaluation of Defensive Methods for DNNs against Multiple Adversarial Evasion Models 作者:Xinyun Chen, Bo Li, Yevgeniy Vorobeychik
  18. Calibrating Energy-based Generative Adversarial Networks 作者: Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard Hovy, Aaron Courville
  19. Inverse Problems in Computer Vision using Adversarial Imagination Priors 作者: Hsiao-Yu Fish Tung, Katerina Fragkiadaki
  20. Towards Principled Methods for Training Generative Adversarial Networks作者: Martin Arjovsky, Leon Bottou
  21. Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning 作者: Dilin Wang, Qiang Liu
  22. Multi-view Generative Adversarial Networks 作者: Mickaël Chen, Ludovic Denoyer
  23. LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation 作者: Jianwei Yang, Anitha Kannan, Dhruv Batra, Devi Parikh
  24. Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks 作者: Emily Denton, Sam Gross, Rob Fergus
  25. Generative Adversarial Networks for Image Steganography 作者: Denis Volkhonskiy, Boris Borisenko, Evgeny Burnaev
  26. Unrolled Generative Adversarial Networks 作者: Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein
  27. Generative Multi-Adversarial Networks 作者: Ishan Durugkar, Ian Gemp, Sridhar Mahadevan
  28. Joint Multimodal Learning with Deep Generative Models 作者: Masahiro Suzuki, Kotaro Nakayama, Yutaka Matsuo
  29. Fast Adaptation in Generative Models with Generative Matching Networks 作者: Sergey Bartunov, Dmitry P. Vetrov
  30. Adversarially Learned Inference 作者: Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martin Arjovsky, Olivier Mastropietro, Aaron Courville
  31. Perception Updating Networks: On architectural constraints for interpretable video generative models 作者: Eder Santana, Jose C Principe
  32. Energy-based Generative Adversarial Networks 作者:Junbo Zhao, Michael Mathieu, Yann LeCun
  33. Simple Black-Box Adversarial Perturbations for Deep Networks 作者: Nina Narodytska, Shiva Kasiviswanathan
  34. Learning in Implicit Generative Models 作者: Shakir Mohamed, Balaji Lakshminarayanan
  35. On Detecting Adversarial Perturbations 作者: Jan Hendrik Metzen, Tim Genewein, Volker Fischer, Bastian Bischoff
  36. Delving into Transferable Adversarial Examples and Black-box Attacks 作者: Yanpei Liu, Xinyun Chen, Chang Liu, Dawn Song
  37. Adversarial Feature Learning 作者:Jeff Donahue, Philipp Krähenbühl, Trevor Darrell
  38. Generative Paragraph Vector 作者: Ruqing Zhang, Jiafeng Guo, Yanyan Lan, Jun Xu, Xueqi Cheng
  39. Adversarial Machine Learning at Scale 作者: Alexey Kurakin, Ian J. Goodfellow, Samy Bengio
  40. Adversarial Training Methods for Semi-Supervised Text Classification 作者: Takeru Miyato, Andrew M. Dai, Ian Goodfellow
  41. Sampling Generative Networks: Notes on a Few Effective Techniques 作者: Tom White
  42. Adversarial examples in the physical world 作者: Alexey Kurakin, Ian J. Goodfellow, Samy Bengio
  43. Improving Sampling from Generative Autoencoders with Markov Chains 作者:Kai Arulkumaran, Antonia Creswell, Anil Anthony Bharath
  44. Neural Photo Editing with Introspective Adversarial Networks 作者: Andrew Brock, Theodore Lim, J.M. Ritchie, Nick Weston
  45. Learning to Protect Communications with Adversarial Neural Cryptography
  46. 作者: Martín Abadi, David G.

ICLR 2017 -随机/策略梯度论文

以下论文均可在https://amundtveit.com/直接下载

  1. Improving Policy Gradient by Exploring Under-appreciated Rewards 作者:: Ofir Nachum, Mohammad Norouzi, Dale Schuurmans
  2. Leveraging Asynchronicity in Gradient Descent for Scalable Deep Learning 作者:Jeff Daily, Abhinav Vishnu, Charles Siegel
  3. Adding Gradient Noise Improves Learning for Very Deep Networks 作者:: Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Lukasz Kaiser, Karol Kurach, Ilya Sutskever, James Martens
  4. Inefficiency of stochastic gradient descent with larger mini-batches (and more learners) 作者: Onkar Bhardwaj, Guojing Cong
  5. Improving Stochastic Gradient Descent with Feedback 作者: Jayanth Koushik, Hiroaki Hayashi
  6. PGQ: Combining policy gradient and Q-learning 作者: Brendan O’Donoghue, Remi Munos, Koray Kavukcuoglu, Volodymyr Mnih
  7. SGDR: Stochastic Gradient Descent with Restarts 作者: Ilya Loshchilov, Frank Hutter
  8. Neural Data Filter for Bootstrapping Stochastic Gradient Descent 作者: Yang Fan, Fei Tian, Tao Qin, Tie-Yan Liu
  9. Entropy-SGD: Biasing Gradient Descent Into Wide Valleys 作者: Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun (推荐关注)
  10. Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic 作者: Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine
  11. Batch Policy Gradient Methods for Improving Neural Conversation Models 作者:Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter
  12. Training Long Short-Term Memory With Sparsified Stochastic Gradient Descent 作者:: Maohua Zhu, Minsoo Rhu, Jason Clemons, Stephen W. Keckler, Yuan Xie (推荐关注)
  13. Parallel Stochastic Gradient Descent with Sound Combiners 作者: Saeed Maleki, Madanlal Musuvathi, Todd Mytkowicz, Yufei Ding
  14. Gradients of Counterfactuals 作者: Mukund Sundararajan, Ankur Taly, Qiqi Yan

ICLR 2017 — RNN深度学习论文

论文均可在https://amundtveit.com/直接下载

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原文发布于微信公众号 - 新智元(AI_era)

原文发表时间:2016-11-13

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