498 citations · 1.8k across the 31 of their papers we have counts for
5 papers · 1 filter
AlignNet: Unsupervised Entity Alignment
Antonia Creswell, Kyriacos Nikiforou, Oriol Vinyals +8
Recently developed deep learning models are able to learn to segment scenes into component objects without supervision. This opens many new and exciting avenues of research, allowi…
Learning Diverse Representations for Fast Adaptation to Distribution Shift
Daniel Pace, Alessandra Russo, Murray Shanahan
The i.i.d. assumption is a useful idealization that underpins many successful approaches to supervised machine learning. However, its violation can lead to models that learn to exp…
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
Sarthak Mittal, Alex Lamb, Anirudh Goyal +5
Robust perception relies on both bottom-up and top-down signals. Bottom-up signals consist of what's directly observed through sensation. Top-down signals consist of beliefs and ex…
Systematic Generalisation through Task Temporal Logic and Deep Reinforcement Learning
Borja G. León, Murray Shanahan, Francesco Belardinelli
This work introduces a neuro-symbolic agent that combines deep reinforcement learning (DRL) with temporal logic (TL) to achieve systematic zero-shot, i.e., never-seen-before, gener…
Continual Reinforcement Learning with Multi-Timescale Replay
Christos Kaplanis, Claudia Clopath, Murray Shanahan
In this paper, we propose a multi-timescale replay (MTR) buffer for improving continual learning in RL agents faced with environments that are changing continuously over time at ti…