Papers
Learning protein sequence embeddings using information from structure
Tristan Bepler, Bonnie Berger
Learning what and where to attend
Drew Linsley, Dan Shiebler, Sven Eberhardt et al.
Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow
Xue Bin Peng, Angjoo Kanazawa, Sam Toyer et al.
How to train your MAML
Antreas Antoniou, Harrison Edwards, Amos Storkey
Transfer Learning for Sequences via Learning to Collocate
Wanyun Cui, Guangyu Zheng, Zhiqiang Shen et al.
Learning Implicitly Recurrent CNNs Through Parameter Sharing
Pedro Savarese, Michael Maire
Learning Actionable Representations with Goal Conditioned Policies
Dibya Ghosh, Abhishek Gupta, Sergey Levine
A Statistical Approach to Assessing Neural Network Robustness
Stefan Webb, Tom Rainforth, Yee Whye Teh et al.
Random mesh projectors for inverse problems
Konik Kothari*, Sidharth Gupta*, Maarten v. de Hoop et al.
Adaptive Gradient Methods with Dynamic Bound of Learning Rate
Liangchen Luo, Yuanhao Xiong, Yan Liu et al.
Adversarial Attacks on Graph Neural Networks via Meta Learning
Daniel Zügner, Stephan Günnemann
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
George Tucker, Dieterich Lawson, Shixiang Gu et al.
Pay Less Attention with Lightweight and Dynamic Convolutions
Felix Wu, Angela Fan, Alexei Baevski et al.
Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency
Liqian Ma, Xu Jia, Stamatios Georgoulis et al.
Deep Anomaly Detection with Outlier Exposure
Dan Hendrycks, Mantas Mazeika, Thomas Dietterich
Stable Recurrent Models
John Miller, Moritz Hardt
Learning Mixed-Curvature Representations in Product Spaces
Albert Gu, Frederic Sala, Beliz Gunel et al.
Learning Programmatically Structured Representations with Perceptor Gradients
Svetlin Penkov, Subramanian Ramamoorthy
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Jonathan Frankle, Michael Carbin
Learning Exploration Policies for Navigation
Tao Chen, Saurabh Gupta, Abhinav Gupta
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
Wieland Brendel, Matthias Bethge
Three Mechanisms of Weight Decay Regularization
Guodong Zhang, Chaoqi Wang, Bowen Xu et al.
Theoretical Analysis of Auto Rate-Tuning by Batch Normalization
Sanjeev Arora, Zhiyuan Li, Kaifeng Lyu
ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees
Hao He, Hao Wang, Guang-He Lee et al.