284 citations · 1.8k across the 21 of their papers we have counts for
21 papers
Solving math word problems with process- and outcome-based feedback
Jonathan Uesato, Nate Kushman, Ramana Kumar +6
Recent work has shown that asking language models to generate reasoning steps improves performance on many reasoning tasks. When moving beyond prompting, this raises the question o…
Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning
Antonia Creswell, Murray Shanahan, Irina Higgins
Large language models (LLMs) have been shown to be capable of impressive few-shot generalisation to new tasks. However, they still tend to perform poorly on multi-step logical reas…
Symmetry-Based Representations for Artificial and Biological General Intelligence
Irina Higgins, Sébastien Racanière, Danilo Rezende
Biological intelligence is remarkable in its ability to produce complex behaviour in many diverse situations through data efficient, generalisable and transferable skill acquisitio…
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Jack W. Rae, Sebastian Borgeaud, Trevor Cai +77
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.…
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision
Irina Higgins, Peter Wirnsberger, Andrew Jaegle +1
A recently proposed class of models attempts to learn latent dynamics from high-dimensional observations, like images, using priors informed by Hamiltonian mechanics. While these m…
Which priors matter? Benchmarking models for learning latent dynamics
Aleksandar Botev, Andrew Jaegle, Peter Wirnsberger +2
Learning dynamics is at the heart of many important applications of machine learning (ML), such as robotics and autonomous driving. In these settings, ML algorithms typically need…