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05/25/2023
Role-Play with Large Language Models
As dialogue agents become increasingly human-like in their performance, ...
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12/07/2022
Talking About Large Language Models
Thanks to rapid progress in artificial intelligence, we have entered an ...
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08/30/2022
Faithful Reasoning Using Large Language Models
Although contemporary large language models (LMs) demonstrate impressive...
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07/15/2022
Sparse Relational Reasoning with Object-Centric Representations
We investigate the composability of soft-rules learned by relational neu...
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05/19/2022
Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning
Large language models (LLMs) have been shown to be capable of impressive...
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02/10/2022
Abstraction for Deep Reinforcement Learning
We characterise the problem of abstraction in the context of deep reinfo...
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12/15/2021
Feature-Attending Recurrent Modules for Generalization in Reinforcement Learning
Deep reinforcement learning (Deep RL) has recently seen significant prog...
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10/18/2021
In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications
We address the problem of building agents whose goal is to satisfy out-o...
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05/27/2021
Encoders and Ensembles for Task-Free Continual Learning
We present an architecture that is effective for continual learning in a...
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03/08/2021
Unsupervised Object-Based Transition Models for 3D Partially Observable Environments
We present a slot-wise, object-based transition model that decomposes a ...
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07/17/2020
AlignNet: Unsupervised Entity Alignment
Recently developed deep learning models are able to learn to segment sce...
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06/30/2020
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
Robust perception relies on both bottom-up and top-down signals. Bottom-...
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06/12/2020
Learning Diverse Representations for Fast Adaptation to Distribution Shift
The i.i.d. assumption is a useful idealization that underpins many succe...
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06/12/2020
Systematic Generalisation through Task Temporal Logic and Deep Reinforcement Learning
This paper presents a neuro-symbolic agent that combines deep reinforcem...
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04/16/2020
Continual Reinforcement Learning with Multi-Timescale Replay
In this paper, we propose a multi-timescale replay (MTR) buffer for impr...
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09/12/2019
The Animal-AI Environment: Training and Testing Animal-Like Artificial Cognition
Recent advances in artificial intelligence have been strongly driven by ...
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05/24/2019
An Explicitly Relational Neural Network Architecture
With a view to bridging the gap between deep learning and symbolic AI, w...
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02/01/2019
Policy Consolidation for Continual Reinforcement Learning
We propose a method for tackling catastrophic forgetting in deep reinfor...
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07/05/2018
Consistent Generative Query Networks
Stochastic video prediction is usually framed as an extrapolation proble...
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07/04/2018
Conditional Neural Processes
Deep neural networks excel at function approximation, yet they are typic...
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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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02/20/2018
Continual Reinforcement Learning with Complex Synapses
Unlike humans, who are capable of continual learning over their lifetime...
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05/18/2017
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning
The problem of sparse rewards is one of the hardest challenges in contem...
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11/08/2016
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
We study a variant of the variational autoencoder model (VAE) with a Gau...
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09/18/2016
Towards Deep Symbolic Reinforcement Learning
Deep reinforcement learning (DRL) brings the power of deep neural networ...
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04/27/2016
Classifying Options for Deep Reinforcement Learning
In this paper we combine one method for hierarchical reinforcement learn...
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04/22/2015
