Papers
Tree-Structured Reinforcement Learning for Sequential Object Localization
Zequn Jie, Xiaodan Liang, Jiashi Feng et al.
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation
Ilija Bogunovic, Jonathan Scarlett, Andreas Krause et al.
Understanding Probabilistic Sparse Gaussian Process Approximations
Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
Wenjie Luo, Yujia Li, Raquel Urtasun et al.
Unified Methods for Exploiting Piecewise Linear Structure in Convex Optimization
Tyler B Johnson, Carlos Guestrin
Unifying Count-Based Exploration and Intrinsic Motivation
Marc Bellemare, Sriram Srinivasan, Georg Ostrovski et al.
Universal Correspondence Network
Christopher B Choy, JunYoung Gwak, Silvio Savarese et al.
Unsupervised Domain Adaptation with Residual Transfer Networks
Mingsheng Long, Han Zhu, Jianmin Wang et al.
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA
Aapo Hyvarinen, Hiroshi Morioka
Unsupervised Learning for Physical Interaction through Video Prediction
Chelsea Finn, Ian Goodfellow, Sergey Levine
Unsupervised Learning from Noisy Networks with Applications to Hi-C Data
Bo Wang, Junjie Zhu, Armin Pourshafeie et al.
Unsupervised Learning of 3D Structure from Images
Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed et al.
Unsupervised Learning of Spoken Language with Visual Context
David Harwath, Antonio Torralba, James Glass
Unsupervised Risk Estimation Using Only Conditional Independence Structure
Jacob Steinhardt, Percy Liang
Using Fast Weights to Attend to the Recent Past
Jimmy Ba, Geoffrey E. Hinton, Volodymyr Mnih et al.
Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model
Zhen Xu, Wen Dong, Sargur N Srihari
Value Iteration Networks
Aviv Tamar, YI WU, Garrett Thomas et al.
Variance Reduction in Stochastic Gradient Langevin Dynamics
Kumar Avinava Dubey, Sashank J. Reddi, Sinead A Williamson et al.
Variational Autoencoder for Deep Learning of Images, Labels and Captions
Yunchen Pu, Zhe Gan, Ricardo Henao et al.
Variational Bayes on Monte Carlo Steroids
Aditya Grover, Stefano Ermon
Variational Inference in Mixed Probabilistic Submodular Models
Josip Djolonga, Sebastian Tschiatschek, Andreas Krause
Variational Information Maximization for Feature Selection
Shuyang Gao, Greg Ver Steeg, Aram Galstyan
Verification Based Solution for Structured MAB Problems
Zohar S Karnin
VIME: Variational Information Maximizing Exploration
Rein Houthooft, Xi Chen, Xi Chen et al.
Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks
Tianfan Xue, Jiajun Wu, Katherine Bouman et al.