Christopher Burgess — accepted AI-conference papers

2019

Multi-Object Representation Learning with Iterative Variational Inference

Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran +3

ICML 2019oral

Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities. Yet most work on representation learning focuses on feature learning without even considering multiple objects, or treats segmentation as an (often su…

Cited by 565 Source ↗PDF ↗code ↗Scholar ↗

2017

DARLA: Improving Zero-Shot Transfer in Reinforcement Learning

Irina Higgins, Arka Pal, Andrei Rusu, Loic Matthey, Christopher Burgess, Alexander Pritzel +3

ICML 2017poster

Domain adaptation is an important open problem in deep reinforcement learning (RL). In many scenarios of interest data is hard to obtain, so agents may learn a source policy in a setting where data is readily available, with the hope that it generalises well to the target domain. We propose a new mu…

Cited by 560 Source ↗PDF ↗Scholar ↗

2017

beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework

Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick +2

ICLR 2017poster

Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to learn and reason in the same way that humans do. We introduce beta-VAE, a new state…

Cited by 6129 Source ↗Scholar ↗