Papers
Don't let your Discriminator be fooled
Brady Zhou, Philipp Krähenbühl
Efficiently testing local optimality and escaping saddles for ReLU networks
Chulhee Yun, Suvrit Sra, Ali Jadbabaie
Diagnosing and Enhancing VAE Models
Bin Dai, David Wipf
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling
Josue Nassar, Scott Linderman, Monica Bugallo et al.
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
Yuping Luo, Huazhe Xu, Yuanzhi Li et al.
Two-Timescale Networks for Nonlinear Value Function Approximation
Wesley Chung, Somjit Nath, Ajin Joseph et al.
Boosting Robustness Certification of Neural Networks
Gagandeep Singh, Timon Gehr, Markus Püschel et al.
Spherical CNNs on Unstructured Grids
Chiyu Max Jiang, Jingwei Huang, Karthik Kashinath et al.
G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space
Qi Meng, Shuxin Zheng, Huishuai Zhang et al.
Beyond Greedy Ranking: Slate Optimization via List-CVAE
Ray Jiang, Sven Gowal, Yuqiu Qian et al.
Toward Understanding the Impact of Staleness in Distributed Machine Learning
Wei Dai, Yi Zhou, Nanqing Dong et al.
GANSynth: Adversarial Neural Audio Synthesis
Jesse Engel, Kumar Krishna Agrawal, Shuo Chen et al.
Characterizing Audio Adversarial Examples Using Temporal Dependency
Zhuolin Yang, Bo Li, Pin-Yu Chen et al.
DISTRIBUTIONAL CONCAVITY REGULARIZATION FOR GANS
Shoichiro Yamaguchi, Masanori Koyama
Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution
Thomas Elsken, Jan Hendrik Metzen, Frank Hutter
On the Sensitivity of Adversarial Robustness to Input Data Distributions
Gavin Weiguang Ding, Kry Yik Chau Lui, Xiaomeng Jin et al.
Active Learning with Partial Feedback
Peiyun Hu, Zachary C. Lipton, Anima Anandkumar et al.
Guiding Policies with Language via Meta-Learning
John D. Co-Reyes, Abhishek Gupta, Suvansh Sanjeev et al.
Learnable Embedding Space for Efficient Neural Architecture Compression
Shengcao Cao, Xiaofang Wang, Kris M. Kitani
MisGAN: Learning from Incomplete Data with Generative Adversarial Networks
Steven Cheng-Xian Li, Bo Jiang, Benjamin Marlin
K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning
Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov et al.
Do Deep Generative Models Know What They Don't Know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh et al.
RelGAN: Relational Generative Adversarial Networks for Text Generation
Weili Nie, Nina Narodytska, Ankit Patel
NOODL: Provable Online Dictionary Learning and Sparse Coding
Sirisha Rambhatla, Xingguo Li, Jarvis Haupt