ICLR 2017

Main Conference - Oral Presentations

  1. Word Representations via Gaussian Embedding, Luke Vilnis and Andrew McCallum
  2. Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN), Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, and Alan Yuille
  3. Deep Structured Output Learning for Unconstrained Text Recognition, Max Jaderberg, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman
  4. Fast Convolutional Nets With fbfft: A GPU Performance Evaluation, Nicolas Vasilache, Jeff Johnson, Michael Mathieu, Soumith Chintala, Serkan Piantino, and Yann LeCun
  5. Reweighted Wake-Sleep, Jorg Bornschein and Yoshua Bengio
  6. The local low-dimensionality of natural images, Olivier Henaff, Johannes Balle, Neil Rabinowitz, and Eero Simoncelli
  7. Memory Networks, Jason Weston, Sumit Chopra, and Antoine Bordes
  8. Object detectors emerge in Deep Scene CNNs, Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba
  9. Neural Machine Translation by Jointly Learning to Align and Translate, Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio

Main Conference - Poster Presentations

  1. FitNets: Hints for Thin Deep Nets, Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio
  2. Techniques for Learning Binary Stochastic Feedforward Neural Networks, Tapani Raiko, Mathias Berglund, Guillaume Alain, and Laurent Dinh
  3. Reweighted Wake-Sleep, Jorg Bornschein and Yoshua Bengio
  4. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs, Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan Yuille
  5. Multiple Object Recognition with Visual Attention, Jimmy Ba, Volodymyr Mnih, and Koray Kavukcuoglu
  6. Joint RNN-Based Greedy Parsing and Word Composition, Joël Legrand and Ronan Collobert
  7. Adam: A Method for Stochastic Optimization, Jimmy Ba and Diederik Kingma
  8. Neural Machine Translation by Jointly Learning to Align and Translate, Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio
  9. Scheduled denoising autoencoders, Krzysztof Geras and Charles Sutton
  10. Embedding Entities and Relations for Learning and Inference in Knowledge Bases, Bishan Yang, Scott Yih, Xiaodong He, Jianfeng Gao, and Li Deng
  11. The local low-dimensionality of natural images, Olivier Henaff, Johannes Balle, Neil Rabinowitz, and Eero Simoncelli
  12. Explaining and Harnessing Adversarial Examples, Ian Goodfellow, Jon Shlens, and Christian Szegedy
  13. Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition, Vadim Lebedev, Yaroslav Ganin, Victor Lempitsky, Maksim Rakhuba, and Ivan Oseledets
  14. Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN), Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, and Alan Yuille
  15. Deep Structured Output Learning for Unconstrained Text Recognition, Max Jaderberg, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman
  16. Zero-bias autoencoders and the benefits of co-adapting features, Kishore Konda, Roland Memisevic, and David Krueger
  17. Automatic Discovery and Optimization of Parts for Image Classification, Sobhan Naderi Parizi, Andrea Vedaldi, Andrew Zisserman, and Pedro Felzenszwalb
  18. Understanding Locally Competitive Networks, Rupesh Srivastava, Jonathan Masci, Faustino Gomez, and Juergen Schmidhuber
  19. Move Evaluation in Go Using Deep Convolutional Neural Networks, Chris Maddison, Aja Huang, Ilya Sutskever, and David Silver
  20. Fast Convolutional Nets With fbfft: A GPU Performance Evaluation, Nicolas Vasilache, Jeff Johnson, Michael Mathieu, Soumith Chintala, Serkan Piantino, and Yann LeCun
  21. Word Representations via Gaussian Embedding, Luke Vilnis and Andrew McCallum
  22. Memory Networks, Jason Weston, Sumit Chopra, and Antoine Bordes
  23. Generative Modeling of Convolutional Neural Networks, Jifeng Dai, Yang Lu, and Ying-Nian Wu
  24. Object detectors emerge in Deep Scene CNNs, Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba

Workshop Papers

  1. Learning Non-deterministic Representations with Energy-based Ensembles, Maruan Al-Shedivat, Emre Neftci, and Gert Cauwenberghs
  2. Diverse Embedding Neural Network Language Models, Kartik Audhkhasi, Abhinav Sethy, and Bhuvana Ramabhadran
  3. Hot Swapping for Online Adaptation of Optimization Hyperparameters, Kevin Bache, Dennis Decoste, and Padhraic Smyth
  4. Representation Learning for cold-start recommendation, Gabriella Contardo, Ludovic Denoyer, and Thierry Artieres
  5. Training Convolutional Networks with Noisy Labels, Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus
  6. Striving for Simplicity: The All Convolutional Net, Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox, and Martin Riedmiller
  7. Training Deep Neural Networks on Noisy Labels with Bootstrapping, Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich
  8. On the Stability of Deep Networks, Raja Giryes, Guillermo Sapiro, and Alex Bronstein
  9. Audio source separation with Discriminative Scattering Networks , Joan Bruna, Yann LeCun, and Pablo Sprechmann
  10. Simple Image Description Generator via a Linear Phrase-Based Model, Pedro Pinheiro, Rémi Lebret, and Ronan Collobert
  11. Embedding Word Similarity with Neural Machine Translation, Felix Hill, Kyunghyun Cho, Sebastien Jean, Coline Devin, and Yoshua Bengio
  12. A Group Theoretic Perspective on Unsupervised Deep Learning, Arnab Paul and Suresh Venkatasubramanian
  13. Learning Longer Memory in Recurrent Neural Networks, Tomas Mikolov, Armand Joulin, Sumit Chopra, Michael Mathieu, and Marc'Aurelio Ranzato
  14. NICE: Non-linear Independent Components Estimation, Laurent Dinh, David Krueger, and Yoshua Bengio
  15. Discovering Hidden Factors of Variation in Deep Networks, Brian Cheung, Jesse Livezey, Arjun Bansal, and Bruno Olshausen
  16. Tailoring Word Embeddings for Bilexical Predictions: An Experimental Comparison, Pranava Swaroop Madhyastha, Xavier Carreras, and Ariadna Quattoni
  17. Algorithmic Robustness for Semi-Supervised (ϵ, γ, τ)-Good Metric Learning, Maria-Irina Nicolae, Marc Sebban, Amaury Habrard, Éric Gaussier, and Massih-Reza Amini
  18. Real-World Font Recognition Using Deep Network and Domain Adaptation, Zhangyang Wang, Jianchao Yang, Hailin Jin, Eli Shechtman, Aseem Agarwala, Jon Brandt, and Thomas Huang
  19. Score Function Features for Discriminative Learning, Majid Janzamin, Hanie Sedghi, and Anima Anandkumar
  20. Parallel training of DNNs with Natural Gradient and Parameter Averaging, Daniel Povey, Xioahui Zhang, and Sanjeev Khudanpur
  21. A Generative Model for Deep Convolutional Learning, Yunchen Pu, Xin Yuan, and Lawrence Carin
  22. Random Forests Can Hash, Qiang Qiu, Guillermo Sapiro, and Alex Bronstein
  23. Deep learning with Elastic Averaging SGD, Sixin Zhang, Anna Choromanska, and Yann LeCun
  24. Example Selection For Dictionary Learning, Tomoki Tsuchida and Garrison Cottrell
  25. Permutohedral Lattice CNNs, Martin Kiefel, Varun Jampani, and Peter Gehler
  1. Learning Activation Functions to Improve Deep Neural Networks, Forest Agostinelli, Matthew Hoffman, Peter Sadowski, and Pierre Baldi |
  2. Learning Deep Structured Models, Liang-Chieh Chen, Alexander Schwing, Alan Yuille, and Raquel Urtasun
  3. Low precision arithmetic for deep learning, Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David
  4. Theano-based Large-Scale Visual Recognition with Multiple GPUs, Weiguang Ding, Ruoyan Wang, Fei Mao, and Graham Taylor
  5. Variational Recurrent Auto-Encoders, Otto Fabius and Joost van Amersfoort
  6. Learning Compact Convolutional Neural Networks with Nested Dropout, Chelsea Finn, Lisa Anne Hendricks, and Trevor Darrell
  7. Unsupervised Feature Learning from Temporal Data, Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, and Yann LeCun
  8. Classifier with Hierarchical Topographical Maps as Internal Representation, Pitoyo Hartono, Paul Hollensen, and Thomas Trappenberg
  9. Flattened Convolutional Neural Networks for Feedforward Acceleration, Jonghoon Jin, Aysegul Dundar, and Eugenio Culurciello
  10. Gradual Training Method for Denoising Auto Encoders, Alexander Kalmanovich and Gal Chechik
  11. Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet, Matthias Kümmerer, Lucas Theis, and Matthias Bethge
  12. Difference Target Propagation, Dong-Hyun Lee, Saizheng Zhang, Asja Fischer, Antoine Biard, and Yoshua Bengio
  13. Purine: A Bi-Graph based deep learning framework, Min Lin, Shuo Li, Xuan Luo, and Shuicheng Yan
  14. Pixel-wise Deep Learning for Contour Detection, Jyh-Jing Hwang and Tyng-Luh Liu
  15. Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews, Grégoire Mesnil, Tomas Mikolov, Marc'Aurelio Ranzato, and Yoshua Bengio
  16. Fast Label Embeddings for Extremely Large Output Spaces, Paul Mineiro and Nikos Karampatziakis
  17. An Analysis of Unsupervised Pre-training in Light of Recent Advances, Tom Paine, Pooya Khorrami, Wei Han, and Thomas Huang
  18. Fully Convolutional Multi-Class Multiple Instance Learning, Deepak Pathak, Evan Shelhamer, Jonathan Long, and Trevor Darrell
  19. What Do Deep CNNs Learn About Objects?, Xingchao Peng, Baochen Sun, Karim Ali, and Kate Saenko
  20. Representation using the Weyl Transform, Qiang Qiu, Andrew Thompson, Robert Calderbank, and Guillermo Sapiro
  21. Explorations on high dimensional landscapes, Levent Sagun, Ugur Guney, and Yann LeCun
  22. Generative Class-conditional Autoencoders, Jan Rudy and Graham Taylor
  23. Attention for Fine-Grained Categorization, Pierre Sermanet, Andrea Frome, and Esteban Real
  24. A Baseline for Visual Instance Retrieval with Deep Convolutional Networks, Ali Sharif Razavian, Josephine Sullivan, Atsuto Maki, and Stefan Carlsson
  25. Visual Scene Representation: Scaling and Occlusion, Stefano Soatto, Jingming Dong, and Nikolaos Karianakis
  26. Deep networks with large output spaces, Sudheendra Vijayanarasimhan, Jon Shlens, Jay Yagnik, and Rajat Monga
  27. Self-informed neural network structure learning, David Warde-Farley, Andrew Rabinovich, and Dragomir Anguelov