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05/22/2023
Meta-in-context learning in large language models
Large language models have shown tremendous performance in a variety of ...
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04/12/2023
Meta-Learned Models of Cognition
Meta-learning is a framework for learning learning algorithms through re...
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04/12/2023
DiscoGen: Learning to Discover Gene Regulatory Networks
Accurately inferring Gene Regulatory Networks (GRNs) is a critical and c...
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10/15/2022
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution
Neuroscience has long been an important driver of progress in artificial...
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02/21/2022
HCMD-zero: Learning Value Aligned Mechanisms from Data
Artificial learning agents are mediating a larger and larger number of i...
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02/15/2022
General-purpose, long-context autoregressive modeling with Perceiver AR
Real-world data is high-dimensional: a book, image, or musical performan...
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01/27/2022
Human-centered mechanism design with Democratic AI
Building artificial intelligence (AI) that aligns with human values is a...
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06/07/2021
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition
To help agents reason about scenes in terms of their building blocks, we...
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02/04/2021
Alchemy: A structured task distribution for meta-reinforcement learning
There has been rapidly growing interest in meta-learning as a method for...
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10/18/2020
Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences
Game theoretic views of convention generally rest on notions of common k...
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07/07/2020
Deep Reinforcement Learning and its Neuroscientific Implications
The emergence of powerful artificial intelligence is defining new resear...
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04/24/2020
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget
In many applications, it is desirable to extract only the relevant infor...
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01/29/2020
MEMO: A Deep Network for Flexible Combination of Episodic Memories
Recent research developing neural network architectures with external me...
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10/01/2019
Emergent Systematic Generalization in a Situated Agent
The question of whether deep neural networks are good at generalising be...
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05/08/2019
Meta-learning of Sequential Strategies
In this report we review memory-based meta-learning as a tool for buildi...
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04/18/2019
Is coding a relevant metaphor for building AI? A commentary on "Is coding a relevant metaphor for the brain?", by Romain Brette
Brette contends that the neural coding metaphor is an invalid basis for ...
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03/01/2019
Multi-Object Representation Learning with Iterative Variational Inference
Human perception is structured around objects which form the basis for o...
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01/30/2019
InfoBot: Transfer and Exploration via the Information Bottleneck
A central challenge in reinforcement learning is discovering effective p...
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01/23/2019
Causal Reasoning from Meta-reinforcement Learning
Discovering and exploiting the causal structure in the environment is a ...
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11/04/2018
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
When observing the actions of others, humans carry out inferences about ...
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09/28/2018
Relational Forward Models for Multi-Agent Learning
The behavioral dynamics of multi-agent systems have a rich and orderly s...
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06/05/2018
Relational Deep Reinforcement Learning
We introduce an approach for deep reinforcement learning (RL) that impro...
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05/24/2018
Been There, Done That: Meta-Learning with Episodic Recall
Meta-learning agents excel at rapidly learning new tasks from open-ended...
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03/19/2018
On the importance of single directions for generalization
Despite their ability to memorize large datasets, deep neural networks o...
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02/21/2018
Machine Theory of Mind
Theory of mind (ToM; Premack & Woodruff, 1978) broadly refers to humans'...
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07/26/2017
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
Domain adaptation is an important open problem in deep reinforcement lea...
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07/11/2017