498 citations · 1.8k across the 31 of their papers we have counts for
16 papers · 1 filter
Sparse Relational Reasoning with Object-Centric Representations
Alex F. Spies, Alessandra Russo, Murray Shanahan
We investigate the composability of soft-rules learned by relational neural architectures when operating over object-centric (slot-based) representations, under a variety of sparsi…
Abstraction for Deep Reinforcement Learning
Murray Shanahan, Melanie Mitchell
We characterise the problem of abstraction in the context of deep reinforcement learning. Various well established approaches to analogical reasoning and associative memory might b…
Feature-Attending Recurrent Modules for Generalization in Reinforcement Learning
Wilka Carvalho, Andrew Lampinen, Kyriacos Nikiforou +2
Many important tasks are defined in terms of object. To generalize across these tasks, a reinforcement learning (RL) agent needs to exploit the structure that the objects induce. P…
Encoders and Ensembles for Task-Free Continual Learning
Murray Shanahan, Christos Kaplanis, Jovana Mitrović
We present an architecture that is effective for continual learning in an especially demanding setting, where task boundaries do not exist or are unknown, and where classes have to…
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…