Word Representations via Gaussian Embedding, Luke Vilnis and Andrew McCallum

Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN), Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, and Alan Yuille

Deep Structured Output Learning for Unconstrained Text Recognition, Max Jaderberg, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman

Very Deep Convolutional Networks for Large-Scale Image Recognition, Karen Simonyan and Andrew Zisserman

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation, Nicolas Vasilache, Jeff Johnson, Michael Mathieu, Soumith Chintala, Serkan Piantino, and Yann LeCun

Reweighted Wake-Sleep, Jorg Bornschein and Yoshua Bengio

The local low-dimensionality of natural images, Olivier Henaff, Johannes Balle, Neil Rabinowitz, and Eero Simoncelli

Memory Networks, Jason Weston, Sumit Chopra, and Antoine Bordes

Object detectors emerge in Deep Scene CNNs, Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba

Qualitatively characterizing neural network optimization problems, Ian Goodfellow and Oriol Vinyals

Neural Machine Translation by Jointly Learning to Align and Translate, Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio

FitNets: Hints for Thin Deep Nets, Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio

Techniques for Learning Binary Stochastic Feedforward Neural Networks, Tapani Raiko, Mathias Berglund, Guillaume Alain, and Laurent Dinh

Reweighted Wake-Sleep, Jorg Bornschein and Yoshua Bengio

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs, Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan Yuille

Multiple Object Recognition with Visual Attention, Jimmy Ba, Volodymyr Mnih, and Koray Kavukcuoglu

Deep Narrow Boltzmann Machines are Universal Approximators, Guido Montufar

Transformation Properties of Learned Visual Representations, Taco Cohen and Max Welling

Joint RNN-Based Greedy Parsing and Word Composition, Joël Legrand and Ronan Collobert

Adam: A Method for Stochastic Optimization, Jimmy Ba and Diederik Kingma

Neural Machine Translation by Jointly Learning to Align and Translate, Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio

Scheduled denoising autoencoders, Krzysztof Geras and Charles Sutton

Embedding Entities and Relations for Learning and Inference in Knowledge Bases, Bishan Yang, Scott Yih, Xiaodong He, Jianfeng Gao, and Li Deng

The local low-dimensionality of natural images, Olivier Henaff, Johannes Balle, Neil Rabinowitz, and Eero Simoncelli

Explaining and Harnessing Adversarial Examples, Ian Goodfellow, Jon Shlens, and Christian Szegedy

Modeling Compositionality with Multiplicative Recurrent Neural Networks, Ozan Irsoy and Claire Cardie

Very Deep Convolutional Networks for Large-Scale Image Recognition, Karen Simonyan and Andrew Zisserman

Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition, Vadim Lebedev, Yaroslav Ganin, Victor Lempitsky, Maksim Rakhuba, and Ivan Oseledets

Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN), Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, and Alan Yuille

Deep Structured Output Learning for Unconstrained Text Recognition, Max Jaderberg, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman

Zero-bias autoencoders and the benefits of co-adapting features, Kishore Konda, Roland Memisevic, and David Krueger

Automatic Discovery and Optimization of Parts for Image Classification, Sobhan Naderi Parizi, Andrea Vedaldi, Andrew Zisserman, and Pedro Felzenszwalb

Understanding Locally Competitive Networks, Rupesh Srivastava, Jonathan Masci, Faustino Gomez, and Juergen Schmidhuber

Leveraging Monolingual Data for Crosslingual Compositional Word Representations, Hubert Soyer, Pontus Stenetorp, and Akiko Aizawa

Move Evaluation in Go Using Deep Convolutional Neural Networks, Chris Maddison, Aja Huang, Ilya Sutskever, and David Silver

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation, Nicolas Vasilache, Jeff Johnson, Michael Mathieu, Soumith Chintala, Serkan Piantino, and Yann LeCun

Word Representations via Gaussian Embedding, Luke Vilnis and Andrew McCallum

Qualitatively characterizing neural network optimization problems, Ian Goodfellow and Oriol Vinyals

Memory Networks, Jason Weston, Sumit Chopra, and Antoine Bordes

Generative Modeling of Convolutional Neural Networks, Jifeng Dai, Yang Lu, and Ying-Nian Wu

A Unified Perspective on Multi-Domain and Multi-Task Learning, Yongxin Yang and Timothy Hospedales

Object detectors emerge in Deep Scene CNNs, Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba

Learning Non-deterministic Representations with Energy-based Ensembles, Maruan Al-Shedivat, Emre Neftci, and Gert Cauwenberghs

Diverse Embedding Neural Network Language Models, Kartik Audhkhasi, Abhinav Sethy, and Bhuvana Ramabhadran

Hot Swapping for Online Adaptation of Optimization Hyperparameters, Kevin Bache, Dennis Decoste, and Padhraic Smyth

Representation Learning for cold-start recommendation, Gabriella Contardo, Ludovic Denoyer, and Thierry Artieres

Training Convolutional Networks with Noisy Labels, Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus

Striving for Simplicity: The All Convolutional Net, Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox, and Martin Riedmiller

Learning linearly separable features for speech recognition using convolutional neural networks, Dimitri Palaz, Mathew Magimai Doss, and Ronan Collobert

Training Deep Neural Networks on Noisy Labels with Bootstrapping, Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich

On the Stability of Deep Networks, Raja Giryes, Guillermo Sapiro, and Alex Bronstein

Audio source separation with Discriminative Scattering Networks , Joan Bruna, Yann LeCun, and Pablo Sprechmann

Simple Image Description Generator via a Linear Phrase-Based Model, Pedro Pinheiro, Rémi Lebret, and Ronan Collobert

Stochastic Descent Analysis of Representation Learning Algorithms, Richard Golden

On Distinguishability Criteria for Estimating Generative Models, Ian Goodfellow

Embedding Word Similarity with Neural Machine Translation, Felix Hill, Kyunghyun Cho, Sebastien Jean, Coline Devin, and Yoshua Bengio

Deep metric learning using Triplet network, Elad Hoffer and Nir Ailon

Understanding Minimum Probability Flow for RBMs Under Various Kinds of Dynamics, Daniel Jiwoong Im, Ethan Buchman, and Graham Taylor

A Group Theoretic Perspective on Unsupervised Deep Learning, Arnab Paul and Suresh Venkatasubramanian

Learning Longer Memory in Recurrent Neural Networks, Tomas Mikolov, Armand Joulin, Sumit Chopra, Michael Mathieu, and Marc'Aurelio Ranzato

Inducing Semantic Representation from Text by Jointly Predicting and Factorizing Relations, Ivan Titov and Ehsan Khoddam

NICE: Non-linear Independent Components Estimation, Laurent Dinh, David Krueger, and Yoshua Bengio

Discovering Hidden Factors of Variation in Deep Networks, Brian Cheung, Jesse Livezey, Arjun Bansal, and Bruno Olshausen

Tailoring Word Embeddings for Bilexical Predictions: An Experimental Comparison, Pranava Swaroop Madhyastha, Xavier Carreras, and Ariadna Quattoni

On Learning Vector Representations in Hierarchical Label Spaces, Jinseok Nam and Johannes Fürnkranz

In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning, Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro

Algorithmic Robustness for Semi-Supervised (ϵ, γ, τ)-Good Metric Learning, Maria-Irina Nicolae, Marc Sebban, Amaury Habrard, Éric Gaussier, and Massih-Reza Amini

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

Score Function Features for Discriminative Learning, Majid Janzamin, Hanie Sedghi, and Anima Anandkumar

Parallel training of DNNs with Natural Gradient and Parameter Averaging, Daniel Povey, Xioahui Zhang, and Sanjeev Khudanpur

A Generative Model for Deep Convolutional Learning, Yunchen Pu, Xin Yuan, and Lawrence Carin

Random Forests Can Hash, Qiang Qiu, Guillermo Sapiro, and Alex Bronstein

Provable Methods for Training Neural Networks with Sparse Connectivity, Hanie Sedghi, and Anima Anandkumar

Visual Scene Representations: sufficiency, minimality, invariance and approximation with deep convolutional networks, Stefano Soatto and Alessandro Chiuso |

Deep learning with Elastic Averaging SGD, Sixin Zhang, Anna Choromanska, and Yann LeCun

Example Selection For Dictionary Learning, Tomoki Tsuchida and Garrison Cottrell

Permutohedral Lattice CNNs, Martin Kiefel, Varun Jampani, and Peter Gehler

Unsupervised Domain Adaptation with Feature Embeddings, Yi Yang and Jacob Eisenstein

Weakly Supervised Multi-embeddings Learning of Acoustic Models, Gabriel Synnaeve and Emmanuel Dupoux

Learning Activation Functions to Improve Deep Neural Networks, Forest Agostinelli, Matthew Hoffman, Peter Sadowski, and Pierre Baldi |

Restricted Boltzmann Machine for Classification with Hierarchical Correlated Prior, Gang Chen and Sargur Srihari

Learning Deep Structured Models, Liang-Chieh Chen, Alexander Schwing, Alan Yuille, and Raquel Urtasun

N-gram-Based Low-Dimensional Representation for Document Classification, Rémi Lebret and Ronan Collobert

Low precision arithmetic for deep learning, Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David

Theano-based Large-Scale Visual Recognition with Multiple GPUs, Weiguang Ding, Ruoyan Wang, Fei Mao, and Graham Taylor

Improving zero-shot learning by mitigating the hubness problem, Georgiana Dinu and Marco Baroni

Incorporating Both Distributional and Relational Semantics in Word Representations, Daniel Fried and Kevin Duh

Variational Recurrent Auto-Encoders, Otto Fabius and Joost van Amersfoort

Learning Compact Convolutional Neural Networks with Nested Dropout, Chelsea Finn, Lisa Anne Hendricks, and Trevor Darrell

Compact Part-Based Image Representations: Extremal Competition and Overgeneralization, Marc Goessling and Yali Amit

Unsupervised Feature Learning from Temporal Data, Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, and Yann LeCun

Classifier with Hierarchical Topographical Maps as Internal Representation, Pitoyo Hartono, Paul Hollensen, and Thomas Trappenberg

Entity-Augmented Distributional Semantics for Discourse Relations, Yangfeng Ji and Jacob Eisenstein

Flattened Convolutional Neural Networks for Feedforward Acceleration, Jonghoon Jin, Aysegul Dundar, and Eugenio Culurciello

Gradual Training Method for Denoising Auto Encoders, Alexander Kalmanovich and Gal Chechik

Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet, Matthias Kümmerer, Lucas Theis, and Matthias Bethge

Difference Target Propagation, Dong-Hyun Lee, Saizheng Zhang, Asja Fischer, Antoine Biard, and Yoshua Bengio

Predictive encoding of contextual relationships for perceptual inference, interpolation and prediction, Mingmin Zhao, Chengxu Zhuang, Yizhou Wang, and Tai Sing Lee

Purine: A Bi-Graph based deep learning framework, Min Lin, Shuo Li, Xuan Luo, and Shuicheng Yan

Pixel-wise Deep Learning for Contour Detection, Jyh-Jing Hwang and Tyng-Luh Liu

Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews, Grégoire Mesnil, Tomas Mikolov, Marc'Aurelio Ranzato, and Yoshua Bengio

Fast Label Embeddings for Extremely Large Output Spaces, Paul Mineiro and Nikos Karampatziakis

An Analysis of Unsupervised Pre-training in Light of Recent Advances, Tom Paine, Pooya Khorrami, Wei Han, and Thomas Huang

Fully Convolutional Multi-Class Multiple Instance Learning, Deepak Pathak, Evan Shelhamer, Jonathan Long, and Trevor Darrell

What Do Deep CNNs Learn About Objects?, Xingchao Peng, Baochen Sun, Karim Ali, and Kate Saenko

Representation using the Weyl Transform, Qiang Qiu, Andrew Thompson, Robert Calderbank, and Guillermo Sapiro

Denoising autoencoder with modulated lateral connections learns invariant representations of natural images, Antti Rasmus, Harri Valpola, and Tapani Raiko

Towards Deep Neural Network Architectures Robust to Adversarial Examples, Shixiang Gu and Luca Rigazio

Explorations on high dimensional landscapes, Levent Sagun, Ugur Guney, and Yann LeCun

Generative Class-conditional Autoencoders, Jan Rudy and Graham Taylor

Attention for Fine-Grained Categorization, Pierre Sermanet, Andrea Frome, and Esteban Real

A Baseline for Visual Instance Retrieval with Deep Convolutional Networks, Ali Sharif Razavian, Josephine Sullivan, Atsuto Maki, and Stefan Carlsson

Visual Scene Representation: Scaling and Occlusion, Stefano Soatto, Jingming Dong, and Nikolaos Karianakis

Deep networks with large output spaces, Sudheendra Vijayanarasimhan, Jon Shlens, Jay Yagnik, and Rajat Monga

Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets, Pascal Vincent

Self-informed neural network structure learning, David Warde-Farley, Andrew Rabinovich, and Dragomir Anguelov