79 citations · 93 across the 5 of their papers we have counts for
6 papers
Formalising Concepts as Grounded Abstractions
Stephen Clark, Alexander Lerchner, Tamara von Glehn +4
The notion of concept has been studied for centuries, by philosophers, linguists, cognitive scientists, and researchers in artificial intelligence (Margolis & Laurence, 1999). Ther…
Grounded Language Learning Fast and Slow
Felix Hill, Olivier Tieleman, Tamara von Glehn +3
Recent work has shown that large text-based neural language models, trained with conventional supervised learning objectives, acquire a surprising propensity for few- and one-shot…
Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning
Angeliki Lazaridou, Anna Potapenko, Olivier Tieleman
We present a method for combining multi-agent communication and traditional data-driven approaches to natural language learning, with an end goal of teaching agents to communicate…
Never Give Up: Learning Directed Exploration Strategies
Adrià Puigdomènech Badia, Pablo Sprechmann, Alex Vitvitskyi +8
We propose a reinforcement learning agent to solve hard exploration games by learning a range of directed exploratory policies. We construct an episodic memory-based intrinsic rewa…
Shaping representations through communication: community size effect in artificial learning systems
Olivier Tieleman, Angeliki Lazaridou, Shibl Mourad +2
Motivated by theories of language and communication that explain why communities with large numbers of speakers have, on average, simpler languages with more regularity, we cast th…
A Factorial Mixture Prior for Compositional Deep Generative Models
Ulrich Paquet, Sumedh K. Ghaisas, Olivier Tieleman
We assume that a high-dimensional datum, like an image, is a compositional expression of a set of properties, with a complicated non-linear relationship between the datum and its p…