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09/28/2021
Faster Improvement Rate Population Based Training
The successful training of neural networks typically involves careful an...
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07/27/2021
Open-Ended Learning Leads to Generally Capable Agents
In this work we create agents that can perform well beyond a single, ind...
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06/26/2020
Perception-Prediction-Reaction Agents for Deep Reinforcement Learning
We introduce a new recurrent agent architecture and associated auxiliary...
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04/20/2020
Real World Games Look Like Spinning Tops
This paper investigates the geometrical properties of real world games (...
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12/16/2019
A Deep Neural Network's Loss Surface Contains Every Low-dimensional Pattern
The work "Loss Landscape Sightseeing with Multi-Point Optimization" (Sko...
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10/13/2019
Stabilizing Transformers for Reinforcement Learning
Owing to their ability to both effectively integrate information over lo...
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02/06/2019
Distilling Policy Distillation
The transfer of knowledge from one policy to another is an important too...
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02/05/2019
A Generalized Framework for Population Based Training
Population Based Training (PBT) is a recent approach that jointly optimi...
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01/23/2019
Open-ended Learning in Symmetric Zero-sum Games
Zero-sum games such as chess and poker are, abstractly, functions that e...
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07/03/2018
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
Recent progress in artificial intelligence through reinforcement learnin...
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06/05/2018
Mix&Match - Agent Curricula for Reinforcement Learning
We introduce Mix&Match (M&M) - a training framework designed to facilita...
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11/27/2017
Population Based Training of Neural Networks
Neural networks dominate the modern machine learning landscape, but thei...
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06/20/2017
Grounded Language Learning in a Simulated 3D World
We are increasingly surrounded by artificially intelligent technology th...
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06/16/2017
Value-Decomposition Networks For Cooperative Multi-Agent Learning
We study the problem of cooperative multi-agent reinforcement learning w...
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03/01/2017
Understanding Synthetic Gradients and Decoupled Neural Interfaces
When training neural networks, the use of Synthetic Gradients (SG) allow...
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11/16/2016
Reinforcement Learning with Unsupervised Auxiliary Tasks
Deep reinforcement learning agents have achieved state-of-the-art result...
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08/18/2016
Decoupled Neural Interfaces using Synthetic Gradients
Training directed neural networks typically requires forward-propagating...
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07/03/2016
Unsupervised Learning of 3D Structure from Images
A key goal of computer vision is to recover the underlying 3D structure ...
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06/08/2016
Convolution by Evolution: Differentiable Pattern Producing Networks
In this work we introduce a differentiable version of the Compositional ...
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06/05/2015
Spatial Transformer Networks
Convolutional Neural Networks define an exceptionally powerful class of ...
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12/18/2014
Deep Structured Output Learning for Unconstrained Text Recognition
We develop a representation suitable for the unconstrained recognition o...
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12/04/2014
Reading Text in the Wild with Convolutional Neural Networks
In this work we present an end-to-end system for text spotting -- locali...
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06/09/2014
Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
In this work we present a framework for the recognition of natural scene...
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05/15/2014
