research
∙
05/02/2023
Unlocking the Power of Representations in Long-term Novelty-based Exploration
We introduce Robust Exploration via Clustering-based Online Density Esti...
share
research
∙
02/20/2023
Neural Algorithmic Reasoning with Causal Regularisation
Recent work on neural algorithmic reasoning has investigated the reasoni...
research
∙
01/12/2023
SemPPL: Predicting pseudo-labels for better contrastive representations
Learning from large amounts of unsupervised data and a small amount of s...
research
∙
09/22/2022
A Generalist Neural Algorithmic Learner
The cornerstone of neural algorithmic reasoning is the ability to solve ...
research
∙
09/15/2022
Human-level Atari 200x faster
The task of building general agents that perform well over a wide range ...
research
∙
05/31/2022
The CLRS Algorithmic Reasoning Benchmark
Learning representations of algorithms is an emerging area of machine le...
research
∙
02/17/2022
Retrieval-Augmented Reinforcement Learning
Most deep reinforcement learning (RL) algorithms distill experience into...
research
∙
01/13/2022
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?
Despite recent progress made by self-supervised methods in representatio...
research
∙
11/16/2021
Normalizing flows for atomic solids
We present a machine-learning approach, based on normalizing flows, for ...
research
∙
07/12/2021
CoBERL: Contrastive BERT for Reinforcement Learning
Many reinforcement learning (RL) agents require a large amount of experi...
research
∙
07/12/2021
PonderNet: Learning to Ponder
In standard neural networks the amount of computation used grows with th...
research
∙
06/21/2021
Emphatic Algorithms for Deep Reinforcement Learning
Off-policy learning allows us to learn about possible policies of behavi...
research
∙
05/06/2021
Neural Algorithmic Reasoning
Algorithms have been fundamental to recent global technological advances...
research
∙
03/02/2021
Neural Production Systems
Visual environments are structured, consisting of distinct objects or en...
research
∙
03/01/2021
Coordination Among Neural Modules Through a Shared Global Workspace
Deep learning has seen a movement away from representing examples with a...
research
∙
03/01/2021
Persistent Message Passing
Graph neural networks (GNNs) are a powerful inductive bias for modelling...
research
∙
10/15/2020
Representation Learning via Invariant Causal Mechanisms
Self-supervised learning has emerged as a strategy to reduce the relianc...
research
∙
06/29/2020
Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems
Modeling a structured, dynamic environment like a video game requires ke...
research
∙
06/11/2020
Pointer Graph Networks
Graph neural networks (GNNs) are typically applied to static graphs that...
research
∙
03/30/2020
Agent57: Outperforming the Atari Human Benchmark
Atari games have been a long-standing benchmark in the reinforcement lea...
research
∙
02/14/2020
Never Give Up: Learning Directed Exploration Strategies
We propose a reinforcement learning agent to solve hard exploration game...
research
∙
02/12/2020
Targeted free energy estimation via learned mappings
Free energy perturbation (FEP) was proposed by Zwanzig more than six dec...
research
∙
01/29/2020
MEMO: A Deep Network for Flexible Combination of Episodic Memories
Recent research developing neural network architectures with external me...
research
∙
12/12/2019
Shaping representations through communication: community size effect in artificial learning systems
Motivated by theories of language and communication that explain why com...
research
∙
10/29/2019
Generalization of Reinforcement Learners with Working and Episodic Memory
Memory is an important aspect of intelligence and plays a role in many d...
research
∙
10/23/2019
Neural Execution of Graph Algorithms
Graph Neural Networks (GNNs) are a powerful representational tool for so...
research
∙
10/18/2018
Fast deep reinforcement learning using online adjustments from the past
We propose Ephemeral Value Adjusments (EVA): a means of allowing deep re...
research
∙
05/24/2018
Been There, Done That: Meta-Learning with Episodic Recall
Meta-learning agents excel at rapidly learning new tasks from open-ended...
research
∙
05/23/2018
Pushing the bounds of dropout
We show that dropout training is best understood as performing MAP estim...
research
∙
02/28/2018
Memory-based Parameter Adaptation
Deep neural networks have excelled on a wide range of problems, from vis...
research
∙
07/26/2017
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
Domain adaptation is an important open problem in deep reinforcement lea...
research
∙
06/30/2017
Noisy Networks for Exploration
We introduce NoisyNet, a deep reinforcement learning agent with parametr...
research
∙
04/10/2017
Bayesian Recurrent Neural Networks
In this work we explore a straightforward variational Bayes scheme for R...
research
∙
03/06/2017
Neural Episodic Control
Deep reinforcement learning methods attain super-human performance in a ...
research
∙
01/30/2017
PathNet: Evolution Channels Gradient Descent in Super Neural Networks
For artificial general intelligence (AGI) it would be efficient if multi...
research
∙
11/17/2016
Learning to reinforcement learn
In recent years deep reinforcement learning (RL) systems have attained s...
research
∙
06/17/2016
Early Visual Concept Learning with Unsupervised Deep Learning
Automated discovery of early visual concepts from raw image data is a ma...
research
∙
06/14/2016
Model-Free Episodic Control
State of the art deep reinforcement learning algorithms take many millio...
research
∙
06/13/2016
Matching Networks for One Shot Learning
Learning from a few examples remains a key challenge in machine learning...
research
∙
02/15/2016
Deep Exploration via Bootstrapped DQN
Efficient exploration in complex environments remains a major challenge ...
research
∙
12/31/2015
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server
This paper makes two contributions to Bayesian machine learning algorith...
research
∙
05/20/2015
Weight Uncertainty in Neural Networks
We introduce a new, efficient, principled and backpropagation-compatible...
research
∙
11/11/2014
The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation
We present the Bayesian Echo Chamber, a new Bayesian generative model fo...
research
∙
03/15/2012
Bayesian Rose Trees
Hierarchical structure is ubiquitous in data across many domains. There ...
research
∙
08/31/2010
