Christopher P Burgess | DeepAI

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Featured Co-authors

Alessandro Achille
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Antonia Creswell
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Loic Matthey
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∙ 07/14/2023

Linking vision and motion for self-supervised object-centric perception

Object-centric representations enable autonomous driving algorithms to r...

0 Kaylene C. Stocking, et al. ∙

∙ 07/23/2021

Constellation: Learning relational abstractions over objects for compositional imagination

Learning structured representations of visual scenes is currently a majo...

0 James C. R. Whittington, et al. ∙

∙ 06/07/2021

SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition

To help agents reason about scenes in terms of their building blocks, we...

0 Rishabh Kabra, et al. ∙

∙ 05/29/2019

A Heuristic for Unsupervised Model Selection for Variational Disentangled Representation Learning

Disentangled representations have recently been shown to improve data ef...

0 Sunny Duan, et al. ∙

∙ 05/22/2019

COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration

Data efficiency and robustness to task-irrelevant perturbations are long...

12 Nicholas Watters, et al. ∙

∙ 01/22/2019

MONet: Unsupervised Scene Decomposition and Representation

The ability to decompose scenes in terms of abstract building blocks is ...

0 Christopher P Burgess, et al. ∙

∙ 01/21/2019

Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs

We present a simple neural rendering architecture that helps variational...

0 Nicholas Watters, et al. ∙

∙ 08/20/2018

Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies

Intelligent behaviour in the real-world requires the ability to acquire ...

0 Alessandro Achille, et al. ∙

∙ 04/10/2018

Understanding disentangling in β-VAE

We present new intuitions and theoretical assessments of the emergence o...

0 Christopher P Burgess, et al. ∙

∙ 07/26/2017

DARLA: Improving Zero-Shot Transfer in Reinforcement Learning

Domain adaptation is an important open problem in deep reinforcement lea...

0 Irina Higgins, et al. ∙

∙ 07/11/2017

SCAN: Learning Abstract Hierarchical Compositional Visual Concepts

The natural world is infinitely diverse, yet this diversity arises from ...

0 Irina Higgins, et al. ∙

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