Papers
Learning Approximate Inference Networks for Structured Prediction
Lifu Tu, Kevin Gimpel
Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks
Shankar Krishnan, Ying Xiao, Rif. A. Saurous
Don't Decay the Learning Rate, Increase the Batch Size
Samuel L. Smith, Pieter-Jan Kindermans, Chris Ying et al.
Loss-aware Weight Quantization of Deep Networks
Lu Hou, James T. Kwok
Learning Sparse Neural Networks through L_0 Regularization
Christos Louizos, Max Welling, Diederik P. Kingma
Wasserstein Auto-Encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly et al.
Natural Language Inference over Interaction Space
Yichen Gong, Heng Luo, Jian Zhang
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge
Emmanuel de Bezenac, Arthur Pajot, Patrick Gallinari
Multi-level Residual Networks from Dynamical Systems View
Bo Chang, Lili Meng, Eldad Haber et al.
Multi-View Data Generation Without View Supervision
Mickael Chen, Ludovic Denoyer, Thierry Artières
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs
W. James Murdoch, Peter J. Liu, Bin Yu
Generative Models of Visually Grounded Imagination
Ramakrishna Vedantam, Ian Fischer, Jonathan Huang et al.
MGAN: Training Generative Adversarial Nets with Multiple Generators
Quan Hoang, Tu Dinh Nguyen, Trung Le et al.
Fraternal Dropout
Konrad Zolna, Devansh Arpit, Dendi Suhubdy et al.
Countering Adversarial Images using Input Transformations
Chuan Guo, Mayank Rana, Moustapha Cisse et al.
Temporally Efficient Deep Learning with Spikes
Peter O'Connor, Efstratios Gavves, Matthias Reisser et al.
Compositional Obverter Communication Learning from Raw Visual Input
Edward Choi, Angeliki Lazaridou, Nando de Freitas
Fix your classifier: the marginal value of training the last weight layer
Elad Hoffer, Itay Hubara, Daniel Soudry
AmbientGAN: Generative models from lossy measurements
Ashish Bora, Eric Price, Alexandros G. Dimakis
A Bayesian Perspective on Generalization and Stochastic Gradient Descent
Samuel L. Smith and Quoc V. Le
TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning
Artemij Amiranashvili, Alexey Dosovitskiy, Vladlen Koltun et al.
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
Tsui-Wei Weng*, Huan Zhang*, Pin-Yu Chen et al.
Variational Continual Learning
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui et al.
Variational Network Quantization
Jan Achterhold, Jan Mathias Koehler, Anke Schmeink et al.
Learning to Multi-Task by Active Sampling
Sahil Sharma*, Ashutosh Kumar Jha*, Parikshit S Hegde et al.