21 citations · 58 across the 4 of their papers we have counts for
10 papers
Self-Organizing Intelligent Matter: A blueprint for an AI generating algorithm
Karol Gregor, Frederic Besse
We propose an artificial life framework aimed at facilitating the emergence of intelligent organisms. In this framework there is no explicit notion of an agent: instead there is an…
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…
Shaping Belief States with Generative Environment Models for RL
Karol Gregor, Danilo Jimenez Rezende, Frederic Besse +3
When agents interact with a complex environment, they must form and maintain beliefs about the relevant aspects of that environment. We propose a way to efficiently train expressiv…
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…
Encoding Spatial Relations from Natural Language
Tiago Ramalho, Tomáš Kočiský, Frederic Besse +5
Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fa…
Learning models for visual 3D localization with implicit mapping
Dan Rosenbaum, Frederic Besse, Fabio Viola +2
We consider learning based methods for visual localization that do not require the construction of explicit maps in the form of point clouds or voxels. The goal is to learn an impl…