ICLR 2017

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Accepted Papers (Conference Track)

Oral Presentations

  1. Neural Programmer-Interpreters
    Scott Reed, Nando de Freitas
  2. Regularizing RNNs by Stabilizing Activations
    David Krueger, Roland Memisevic
  3. BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies [code]
    Shihao Ji, Swaminathan Vishwanathan, Nadathur Satish, Michael Anderson, Pradeep Dubey
  4. Towards Universal Paraphrastic Sentence Embeddings [code]
    John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu
  5. Convergent Learning: Do different neural networks learn the same representations?
    Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, John Hopcroft
  6. Net2Net: Accelerating Learning via Knowledge Transfer
    Tianqi Chen, Ian Goodfellow, Jon Shlens
  7. Variational Gaussian Process
    Dustin Tran, Rajesh Ranganath, David Blei
  8. The Variational Fair Autoencoder
    Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard Zemel
  9. A note on the evaluation of generative models
    Lucas Theis, Aäron van den Oord, Matthias Bethge
  10. Neural Networks with Few Multiplications
    Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, Yoshua Bengio
  11. Order-Embeddings of Images and Language [code]
    Ivan Vendrov, Ryan Kiros, Sanja Fidler, Raquel Urtasun

Poster Presentations

  1. Learning to Diagnose with LSTM Recurrent Neural Networks
    Zachary Lipton, David Kale, Charles Elkan, Randall Wetzel
  2. Prioritized Experience Replay
    Tom Schaul, John Quan, Ioannis Antonoglou, David Silver
  3. Importance Weighted Autoencoders
    Yuri Burda, Ruslan Salakhutdinov, Roger Grosse
  4. Variationally Auto-Encoded Deep Gaussian Processes
    Zhenwen Dai, Andreas Damianou, Javier Gonzalez, Neil Lawrence
  5. Training Convolutional Neural Networks with Low-rank Filters for Efficient Image Classification
    Yani Ioannou, Duncan Robertson, Jamie Shotton, roberto Cipolla, Antonio Criminisi, Jamie Shotton
  6. Reducing Overfitting in Deep Networks by Decorrelating Representations
    Michael Cogswell, Faruk Ahmed, Ross Girshick, Larry Zitnick, Dhruv Batra
  7. Generating Images from Captions with Attention
    Elman Mansimov, Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov
  8. Reasoning about Entailment with Neural Attention
    Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Phil Blunsom
  9. Convolutional Neural Networks With Low-rank Regularization
    Cheng Tai, Tong Xiao, Yi Zhang, Xiaogang Wang, Weinan E
  10. Unifying distillation and privileged information
    David Lopez-Paz, Leon Bottou, Bernhard Schölkopf, Vladimir Vapnik
  11. All you need is a good init [code]
    Dmytro Mishkin, Jiri Matas
  12. Bayesian Representation Learning with Oracle Constraints
    Theofanis Karaletsos, Serge Belongie, Gunnar Rätsch
  13. Neural Programmer: Inducing Latent Programs with Gradient Descent
    Arvind Neelakantan, Quoc Le, Ilya Sutskever
  14. SparkNet: Training Deep Networks in Spark
    Philipp Moritz, Robert Nishihara, Ion Stoica, Michael Jordan
  15. MuProp: Unbiased Backpropagation For Stochastic Neural Networks
    Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih
  16. Diversity Networks
    Zelda Mariet, Suvrit Sra
  17. Learning VIsual Predictive Models of Physics for Playing Billiards
    Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, Jitendra Malik
  18. Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks [code] [data]
    Jason Weston, Antoine Bordes, Sumit Chopra, Sasha Rush, Bart van Merrienboer, Armand Joulin, Tomas Mikolov
  19. Evaluating Prerequisite Qualities for Learning End-to-end Dialog Systems [data]
    Jesse Dodge, Andreea Gane, Xiang Zhang, Antoine Bordes, Sumit Chopra, Alexander Miller, Arthur Szlam, Jason Weston
  20. Distributional Smoothing with Virtual Adversarial Training [code]
    Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii
  21. Multi-task Sequence to Sequence Learning
    Minh-Thang Luong, Quoc Le, Ilya Sutskever, Oriol Vinyals, Lukasz Kaiser
  22. A Test of Relative Similarity for Model Selection in Generative Models
    Eugene Belilovsky, Wacha Bounliphone, Matthew Blaschko, Ioannis Antonoglou, Arthur Gretton
  23. Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications
    Yong-Deok Kim, Eunhyeok Park, Sungjoo Yoo, Taelim Choi, Lu Yang, Dongjun Shin
  24. Session-based recommendations with recurrent neural networks [code]
    Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk
  25. Continuous control with deep reinforcement learning
    Timothy Lillicrap, Jonathan Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra
  26. Recurrent Gaussian Processes
    César Lincoln Mattos, Zhenwen Dai, Andreas Damianou, Jeremy Forth, Guilherme Barreto, Neil Lawrence
  27. Auxiliary Image Regularization for Deep CNNs with Noisy Labels
    Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, Trevor Darrell
  28. Policy Distillation
    Andrei Rusu, Sergio Gomez, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, Raia Hadsell
  29. Neural Random-Access Machines
    Karol Kurach, Marcin Andrychowicz, Ilya Sutskever
  30. Gated Graph Sequence Neural Networks
    Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel, CIFAR
  31. Metric Learning with Adaptive Density Discrimination
    Oren Rippel, Manohar Paluri, Piotr Dollar, Lubomir Bourdev
  32. Censoring Representations with an Adversary
    Harrison Edwards, Amos Storkey
  33. Variable Rate Image Compression with Recurrent Neural Networks
    George Toderici, Sean O'Malley, Damien Vincent, Sung Jin Hwang, Michele Covell, Shumeet Baluja, Rahul Sukthankar, David Minnen
  34. Delving Deeper into Convolutional Networks for Learning Video Representations
    Nicolas Ballas, Li Yao, Pal Chris, Aaron Courville
  35. Data-dependent initializations of Convolutional Neural Networks [code]
    Philipp Kraehenbuehl, Carl Doersch, Jeff Donahue, Trevor Darrell
  36. Order Matters: Sequence to sequence for sets
    Oriol Vinyals, Samy Bengio, Manjunath Kudlur
  37. High-Dimensional Continuous Control Using Generalized Advantage Estimation
    John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, Pieter Abbeel
  38. Deep Multi Scale Video Prediction Beyond Mean Square Error
    Michael Mathieu, camille couprie, Yann Lecun
  39. Grid Long Short-Term Memory
    Nal Kalchbrenner, Alex Graves, Ivo Danihelka
  40. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
    Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter
  41. Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning
    Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov
  42. Segmental Recurrent Neural Networks
    Lingpeng Kong, Chris Dyer, Noah Smith
  43. Deep Linear Discriminant Analysis [code]
    Matthias Dorfer, Rainer Kelz, Gerhard Widmer
  44. Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks [code]
    Pouya Bashivan, Irina Rish, Mohammed Yeasin, Noel Codella
  45. Digging Deep into the layers of CNNs: In Search of How CNNs Achieve View Invariance
    Amr Bakry, Mohamed Elhoseiny, Tarek El-Gaaly, Ahmed Elgammal
  46. Data-Dependent Path Normalization in Neural Networks
    Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
  47. Reasoning in Vector Space: An Exploratory Study of Question Answering
    Moontae Lee, Xiaodong He, Wen-tau Yih, Jianfeng Gao, Li Deng, Paul Smolensky
  48. Neural GPUs Learn Algorithms [code] [video]
    Lukasz Kaiser, Ilya Sutskever
  49. ACDC: A Structured Efficient Linear Layer
    Marcin Moczulski, Misha Denil, Jeremy Appleyard, Nando de Freitas
  50. Density Modeling of Images using a Generalized Normalization Transformation
    Johannes Ballé, Valero Laparra, Eero Simoncelli
  51. Adversarial Manipulation of Deep Representations [code]
    Sara Sabour, Yanshuai Cao, Fartash Faghri, David Fleet
  52. Geodesics of learned representations
    Olivier Hénaff, Eero Simoncelli
  53. Sequence Level Training with Recurrent Neural Networks
    Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, Wojciech Zaremba
  54. Super-resolution with deep convolutional sufficient statistics
    Joan Bruna, Pablo Sprechmann, Yann Lecun