Search Results for author: David Saxton

Found 8 papers, 4 papers with code

Analysing Mathematical Reasoning Abilities of Neural Models

6 code implementations ICLR 2019 David Saxton, Edward Grefenstette, Felix Hill, Pushmeet Kohli

The structured nature of the mathematics domain, covering arithmetic, algebra, probability and calculus, enables the construction of training and test splits designed to clearly illuminate the capabilities and failure-modes of different architectures, as well as evaluate their ability to compose and relate knowledge and learned processes.

Mathematical Reasoning Math Word Problem Solving

Meta-Learning surrogate models for sequential decision making

no code implementations28 Mar 2019 Alexandre Galashov, Jonathan Schwarz, Hyunjik Kim, Marta Garnelo, David Saxton, Pushmeet Kohli, S. M. Ali Eslami, Yee Whye Teh

We introduce a unified probabilistic framework for solving sequential decision making problems ranging from Bayesian optimisation to contextual bandits and reinforcement learning.

Bayesian Optimisation Decision Making +4

Can Neural Networks Understand Logical Entailment?

no code implementations ICLR 2018 Richard Evans, David Saxton, David Amos, Pushmeet Kohli, Edward Grefenstette

We introduce a new dataset of logical entailments for the purpose of measuring models' ability to capture and exploit the structure of logical expressions against an entailment prediction task.

Learning objects from pixels

no code implementations ICLR 2018 David Saxton

We show how discrete objects can be learnt in an unsupervised fashion from pixels, and how to perform reinforcement learning using this object representation.

Atari Games reinforcement-learning

Programmable Agents

no code implementations20 Jun 2017 Misha Denil, Sergio Gómez Colmenarejo, Serkan Cabi, David Saxton, Nando de Freitas

We build deep RL agents that execute declarative programs expressed in formal language.

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