2.9k citations · 3k across the 5 of their papers we have counts for
7 papers · 1 filter
Podracer architectures for scalable Reinforcement Learning
Matteo Hessel, Manuel Kroiss, Aidan Clark +5
Supporting state-of-the-art AI research requires balancing rapid prototyping, ease of use, and quick iteration, with the ability to deploy experiments at a scale traditionally asso…
Neural Communication Systems with Bandwidth-limited Channel
Karen Ullrich, Fabio Viola, Danilo Jimenez Rezende
Reliably transmitting messages despite information loss due to a noisy channel is a core problem of information theory. One of the most important aspects of real world communicatio…
Causally Correct Partial Models for Reinforcement Learning
Danilo J. Rezende, Ivo Danihelka, George Papamakarios +11
In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions. However, jointly modeling future observations can b…
Value-driven Hindsight Modelling
Arthur Guez, Fabio Viola, Théophane Weber +5
Value estimation is a critical component of the reinforcement learning (RL) paradigm. The question of how to effectively learn value predictors from data is one of the major proble…
TF-Replicator: Distributed Machine Learning for Researchers
Peter Buchlovsky, David Budden, Dominik Grewe +9
We describe TF-Replicator, a framework for distributed machine learning designed for DeepMind researchers and implemented as an abstraction over TensorFlow. TF-Replicator simplifie…
Neural Processes
Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum +4
A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision. A Gaussian process (GP),…