211 citations · 318 across the 3 of their papers we have counts for
6 papers
Predictive Information Accelerates Learning in RL
Kuang-Huei Lee, Ian Fischer, Anthony Liu +4
The Predictive Information is the mutual information between the past and the future, I(X_past; X_future). We hypothesize that capturing the predictive information is useful in RL,…
Measuring the Reliability of Reinforcement Learning Algorithms
Stephanie C. Y. Chan, Samuel Fishman, John Canny +2
Lack of reliability is a well-known issue for reinforcement learning (RL) algorithms. This problem has gained increasing attention in recent years, and efforts to improve it have g…
From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following
Justin Fu, Anoop Korattikara, Sergey Levine +1
Reinforcement learning is a promising framework for solving control problems, but its use in practical situations is hampered by the fact that reward functions are often difficult…
Tracking Emerges by Colorizing Videos
Carl Vondrick, Abhinav Shrivastava, Alireza Fathi +2
We use large amounts of unlabeled video to learn models for visual tracking without manual human supervision. We leverage the natural temporal coherency of color to create a model…
PixColor: Pixel Recursive Colorization
Sergio Guadarrama, Ryan Dahl, David Bieber +3
We propose a novel approach to automatically produce multiple colorized versions of a grayscale image. Our method results from the observation that the task of automated colorizati…
Semantic Instance Segmentation via Deep Metric Learning
Alireza Fathi, Zbigniew Wojna, Vivek Rathod +4
We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together.…