184 citations · 455 across the 17 of their papers we have counts for
4 papers · 1 filter
Episodic Curiosity through Reachability
Nikolay Savinov, Anton Raichuk, Raphaël Marinier +4
Rewards are sparse in the real world and most of today's reinforcement learning algorithms struggle with such sparsity. One solution to this problem is to allow the agent to create…
Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem
Hugo Penedones, Damien Vincent, Hartmut Maennel +3
Temporal-Difference learning (TD) [Sutton, 1988] with function approximation can converge to solutions that are worse than those obtained by Monte-Carlo regression, even in the sim…
Competitive Training of Mixtures of Independent Deep Generative Models
Francesco Locatello, Damien Vincent, Ilya Tolstikhin +3
A common assumption in causal modeling posits that the data is generated by a set of independent mechanisms, and algorithms should aim to recover this structure. Standard unsupervi…
Spatially adaptive image compression using a tiled deep network
David Minnen, George Toderici, Michele Covell +6
Deep neural networks represent a powerful class of function approximators that can learn to compress and reconstruct images. Existing image compression algorithms based on neural n…