ICLR 2016

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. The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations
    Felix Hill, Antoine Bordes, Sumit Chopra, Jason Weston
  5. Towards Universal Paraphrastic Sentence Embeddings [code]
    John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu
  6. Convergent Learning: Do different neural networks learn the same representations?
    Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, John Hopcroft
  7. Net2Net: Accelerating Learning via Knowledge Transfer
    Tianqi Chen, Ian Goodfellow, Jon Shlens
  8. Variational Gaussian Process
    Dustin Tran, Rajesh Ranganath, David Blei
  9. The Variational Fair Autoencoder
    Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard Zemel
  10. A note on the evaluation of generative models
    Lucas Theis, Aäron van den Oord, Matthias Bethge
  11. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
    Song Han, Huizi Mao, Bill Dally
  12. Neural Networks with Few Multiplications
    Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, Yoshua Bengio
  13. Order-Embeddings of Images and Language [code]
    Ivan Vendrov, Ryan Kiros, Sanja Fidler, Raquel Urtasun
  14. Generating Images from Captions with Attention
    Elman Mansimov, Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov
  15. Density Modeling of Images using a Generalized Normalization Transformation
    Johannes Ballé, Valero Laparra, Eero Simoncelli

Poster Presentations

  1. Multi-Scale Context Aggregation by Dilated Convolutions
    Fisher Yu, Vladlen Koltun
  2. Learning to Diagnose with LSTM Recurrent Neural Networks
    Zachary Lipton, David Kale, Charles Elkan, Randall Wetzel
  3. Prioritized Experience Replay
    Tom Schaul, John Quan, Ioannis Antonoglou, David Silver
  4. Importance Weighted Autoencoders
    Yuri Burda, Ruslan Salakhutdinov, Roger Grosse
  5. Variationally Auto-Encoded Deep Gaussian Processes
    Zhenwen Dai, Andreas Damianou, Javier Gonzalez, Neil Lawrence
  6. Training Convolutional Neural Networks with Low-rank Filters for Efficient Image Classification
    Yani Ioannou, Duncan Robertson, Jamie Shotton, roberto Cipolla, Antonio Criminisi, Jamie Shotton
  7. Reducing Overfitting in Deep Networks by Decorrelating Representations
    Michael Cogswell, Faruk Ahmed, Ross Girshick, Larry Zitnick, Dhruv Batra
  8. Pushing the Boundaries of Boundary Detection using Deep Learning
    Iasonas Kokkinos
  9. Reasoning about Entailment with Neural Attention
    Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Phil Blunsom
  10. Convolutional Neural Networks With Low-rank Regularization
    Cheng Tai, Tong Xiao, Yi Zhang, Xiaogang Wang, Weinan E
  11. Unifying distillation and privileged information
    David Lopez-Paz, Leon Bottou, Bernhard Schölkopf, Vladimir Vapnik
  12. Particular object retrieval with integral max-pooling of CNN activations [code]
    Giorgos Tolias, Ronan Sicre, Hervé Jégou
  13. All you need is a good init [code]
    Dmytro Mishkin, Jiri Matas
  14. Bayesian Representation Learning with Oracle Constraints
    Theofanis Karaletsos, Serge Belongie, Gunnar Rätsch
  15. Neural Programmer: Inducing Latent Programs with Gradient Descent
    Arvind Neelakantan, Quoc Le, Ilya Sutskever
  16. SparkNet: Training Deep Networks in Spark
    Philipp Moritz, Robert Nishihara, Ion Stoica, Michael Jordan
  17. Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
    Jost Tobias Springenberg
  18. MuProp: Unbiased Backpropagation For Stochastic Neural Networks
    Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih
  19. Data Representation and Compression Using Linear-Programming Approximations
    Hristo Paskov, John Mitchell, Trevor Hastie
  20. Diversity Networks
    Zelda Mariet, Suvrit Sra
  21. Deep Reinforcement Learning in Parameterized Action Space [code] [data]
    Matthew Hausknecht, Peter Stone
  22. Learning VIsual Predictive Models of Physics for Playing Billiards
    Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, Jitendra Malik
  23. 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
  24. 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
  25. Better Computer Go Player with Neural Network and Long-term Prediction
    Yuandong Tian, Yan Zhu
  26. Distributional Smoothing with Virtual Adversarial Training [code]
    Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii
  27. Multi-task Sequence to Sequence Learning
    Minh-Thang Luong, Quoc Le, Ilya Sutskever, Oriol Vinyals, Lukasz Kaiser
  28. A Test of Relative Similarity for Model Selection in Generative Models
    Eugene Belilovsky, Wacha Bounliphone, Matthew Blaschko, Ioannis Antonoglou, Arthur Gretton
  29. 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
  30. Session-based recommendations with recurrent neural networks [code]
    Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk
  31. Continuous control with deep reinforcement learning
    Timothy Lillicrap, Jonathan Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra
  32. Recurrent Gaussian Processes
    César Lincoln Mattos, Zhenwen Dai, Andreas Damianou, Jeremy Forth, Guilherme Barreto, Neil Lawrence
  33. Modeling Visual Representations:Defining Properties and Deep Approximations
    Stefano Soatto, Alessandro Chiuso
  34. Auxiliary Image Regularization for Deep CNNs with Noisy Labels
    Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, Trevor Darrell
  35. Policy Distillation
    Andrei Rusu, Sergio Gomez, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, Raia Hadsell
  36. Neural Random-Access Machines
    Karol Kurach, Marcin Andrychowicz, Ilya Sutskever
  37. Gated Graph Sequence Neural Networks
    Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel, CIFAR
  38. Metric Learning with Adaptive Density Discrimination
    Oren Rippel, Manohar Paluri, Piotr Dollar, Lubomir Bourdev
  39. Censoring Representations with an Adversary
    Harrison Edwards, Amos Storkey
  40. 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
  41. Delving Deeper into Convolutional Networks for Learning Video Representations
    Nicolas Ballas, Li Yao, Pal Chris, Aaron Courville
  42. 8-Bit Approximations for Parallelism in Deep Learning
    Tim Dettmers
  43. Data-dependent initializations of Convolutional Neural Networks [code]
    Philipp Kraehenbuehl, Carl Doersch, Jeff Donahue, Trevor Darrell
  44. Order Matters: Sequence to sequence for sets
    Oriol Vinyals, Samy Bengio, Manjunath Kudlur
  45. High-Dimensional Continuous Control Using Generalized Advantage Estimation
    John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, Pieter Abbeel
  46. Deep Multi Scale Video Prediction Beyond Mean Square Error
    Michael Mathieu, camille couprie, Yann Lecun
  47. Grid Long Short-Term Memory
    Nal Kalchbrenner, Alex Graves, Ivo Danihelka
  48. Predicting distributions with Linearizing Belief Networks
    Yann Dauphin, David Grangier
  49. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
    Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter
  50. Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning
    Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov
  51. Segmental Recurrent Neural Networks
    Lingpeng Kong, Chris Dyer, Noah Smith
  52. Deep Linear Discriminant Analysis [code]
    Matthias Dorfer, Rainer Kelz, Gerhard Widmer
  53. Large-Scale Approximate Kernel Canonical Correlation Analysis
    Weiran Wang, Karen Livescu
  54. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
    Alec Radford, Luke Metz, Soumith Chintala
  55. Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks [code]
    Pouya Bashivan, Irina Rish, Mohammed Yeasin, Noel Codella
  56. Digging Deep into the layers of CNNs: In Search of How CNNs Achieve View Invariance
    Amr Bakry, Mohamed Elhoseiny, Tarek El-Gaaly, Ahmed Elgammal
  57. An Exploration of Softmax Alternatives Belonging to the Spherical Loss Family
    Alexandre De Brébisson, Pascal Vincent
  58. Data-Dependent Path Normalization in Neural Networks
    Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
  59. Reasoning in Vector Space: An Exploratory Study of Question Answering
    Moontae Lee, Xiaodong He, Wen-tau Yih, Jianfeng Gao, Li Deng, Paul Smolensky
  60. Neural GPUs Learn Algorithms [code] [video]
    Lukasz Kaiser, Ilya Sutskever
  61. ACDC: A Structured Efficient Linear Layer
    Marcin Moczulski, Misha Denil, Jeremy Appleyard, Nando de Freitas
  62. Adversarial Manipulation of Deep Representations [code]
    Sara Sabour, Yanshuai Cao, Fartash Faghri, David Fleet
  63. Geodesics of learned representations
    Olivier Hénaff, Eero Simoncelli
  64. Sequence Level Training with Recurrent Neural Networks
    Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, Wojciech Zaremba
  65. Super-resolution with deep convolutional sufficient statistics
    Joan Bruna, Pablo Sprechmann, Yann Lecun