Search Results for author: David Amos

Found 6 papers, 3 papers with code

Towards a Definition of Disentangled Representations

1 code implementation5 Dec 2018 Irina Higgins, David Amos, David Pfau, Sebastien Racaniere, Loic Matthey, Danilo Rezende, Alexander Lerchner

Here we propose that a principled solution to characterising disentangled representations can be found by focusing on the transformation properties of the world.

Representation Learning

Probing Physics Knowledge Using Tools from Developmental Psychology

no code implementations3 Apr 2018 Luis Piloto, Ari Weinstein, Dhruva TB, Arun Ahuja, Mehdi Mirza, Greg Wayne, David Amos, Chia-Chun Hung, Matt Botvinick

While some work on this problem has taken the approach of building in components such as ready-made physics engines, other research aims to extract general physical concepts directly from sensory data.

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.

Generative Temporal Models with Memory

no code implementations15 Feb 2017 Mevlana Gemici, Chia-Chun Hung, Adam Santoro, Greg Wayne, Shakir Mohamed, Danilo J. Rezende, David Amos, Timothy Lillicrap

We consider the general problem of modeling temporal data with long-range dependencies, wherein new observations are fully or partially predictable based on temporally-distant, past observations.

Variational Inference

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