Papers
Neural Inverse Rendering for General Reflectance Photometric Stereo
Tatsunori Taniai, Takanori Maehara
D^2: Decentralized Training over Decentralized Data
Hanlin Tang, Xiangru Lian, Ming Yan et al.
Learning the Reward Function for a Misspecified Model
Erik Talvitie
Differentiable Compositional Kernel Learning for Gaussian Processes
Shengyang Sun, Guodong Zhang, Chaoqi Wang et al.
Convolutional Imputation of Matrix Networks
Qingyun Sun, Mengyuan Yan, David Donoho et al.
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation
Qiang Sun, Kean Ming Tan, Han Liu et al.
Scalable approximate Bayesian inference for particle tracking data
Ruoxi Sun, Liam Paninski
Neural Program Synthesis from Diverse Demonstration Videos
Shao-Hua Sun, Hyeonwoo Noh, Sriram Somasundaram et al.
Stagewise Safe Bayesian Optimization with Gaussian Processes
Yanan Sui, Vincent Zhuang, Joel Burdick et al.
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search
Masanori Suganuma, Mete Ozay, Takayuki Okatani
Learning Low-Dimensional Temporal Representations
Bing Su, Ying Wu
Approximation Algorithms for Cascading Prediction Models
Matthew Streeter
Structured Control Nets for Deep Reinforcement Learning
Mario Srouji, Jian Zhang, Ruslan Salakhutdinov
Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control
Aravind Srinivas, Allan Jabri, Pieter Abbeel et al.
Knowledge Transfer with Jacobian Matching
Suraj Srinivas, Francois Fleuret
Accelerating Natural Gradient with Higher-Order Invariance
Yang Song, Jiaming Song, Stefano Ermon
An Inference-Based Policy Gradient Method for Learning Options
Matthew Smith, Herke Hoof, Joelle Pineau
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
RJ Skerry-Ryan, Eric Battenberg, Ying Xiao et al.
K-means clustering using random matrix sparsification
Kaushik Sinha
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
Umut Simsekli, Cagatay Yildiz, Than Huy Nguyen et al.
CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning
Wissam Siblini, Pascale Kuntz, Frank Meyer
TACO: Learning Task Decomposition via Temporal Alignment for Control
Kyriacos Shiarlis, Markus Wulfmeier, Sasha Salter et al.
A Spectral Approach to Gradient Estimation for Implicit Distributions
Jiaxin Shi, Shengyang Sun, Jun Zhu
An Algorithmic Framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-Gradient Method
Li Shen, Peng Sun, Yitong Wang et al.
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Zebang Shen, Aryan Mokhtari, Tengfei Zhou et al.