Christopher P. Burgess
Christopher P. Burgess
AI Researcher
Потвърден имейл адрес: chrisburgess.me.uk
β-VAE: Learning basic visual concepts with a constrained variational framework
I Higgins, L Matthey, A Pal, C Burgess, X Glorot, M Botvinick, S Mohamed, ...
Understanding disentangling in β-VAE
CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
MONet: Unsupervised Scene Decomposition and Representation
CP Burgess, L Matthey, N Watters, R Kabra, I Higgins, M Botvinick, ...
arXiv preprint arXiv:1901.11390, 2019
DARLA: Improving zero-shot transfer in reinforcement learning
I Higgins, A Pal, AA Rusu, L Matthey, CP Burgess, A Pritzel, M Botvinick, ...
arXiv preprint arXiv:1707.08475, 2017
Multi-object representation learning with iterative variational inference
K Greff, RL Kaufman, R Kabra, N Watters, C Burgess, D Zoran, L Matthey, ...
International Conference on Machine Learning, 2424-2433, 2019
A Heuristic for Unsupervised Model Selection for Variational Disentangled Representation Learning.
S Duan, N Watters, L Matthey, CP Burgess, A Lerchner, I Higgins
High-yield methods for accurate two-alternative visual psychophysics in head-fixed mice
CP Burgess, A Lak, NA Steinmetz, P Zatka-Haas, CB Reddy, EAK Jacobs, ...
Cell Reports 20 (10), 2513-2524, 2017
SCAN: learning abstract hierarchical compositional visual concepts
I Higgins, N Sonnerat, L Matthey, A Pal, CP Burgess, M Botvinick, ...
arXiv preprint arXiv:1707.03389, 2017
Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs
N Watters, L Matthey, CP Burgess, A Lerchner
arXiv preprint arXiv:1901.07017, 2019
Life-long disentangled representation learning with cross-domain latent homologies
A Achille, T Eccles, L Matthey, CP Burgess, N Watters, A Lerchner, ...
arXiv preprint arXiv:1808.06508, 2018
COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration
N Watters, L Matthey, M Bosnjak, CP Burgess, A Lerchner
arXiv preprint arXiv:1905.09275, 2019
Unsupervised Model Selection for Variational Disentangled Representation Learning
S Duan, L Matthey, A Saraiva, N Watters, CP Burgess, A Lerchner, ...
arXiv preprint arXiv:1905.12614, 2019
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition
R Kabra, D Zoran, G Erdogan, L Matthey, A Creswell, M Botvinick, ...
arXiv preprint arXiv:2106.03849, 2021
Cortical state fluctuations across layers of V1 during visual spatial perception
A Speed, J Del Rosario, CP Burgess, B Haider
Cell reports 26 (11), 2868-2874. e3, 2019
Controlling phase noise in oscillatory interference models of grid cell firing
CP Burgess, N Burgess
Journal of Neuroscience 34 (18), 6224-6232, 2014
The multi-entity variational autoencoder
C Nash, SMA Eslami, C Burgess, I Higgins, D Zoran, T Weber, P Battaglia
NIPS Workshops, 2017
Unsupervised Object-Based Transition Models for 3D Partially Observable Environments
A Creswell, R Kabra, C Burgess, M Shanahan
arXiv preprint arXiv:2103.04693, 2021
Rigbox: an Open-Source toolbox for probing neurons and behavior
J Bhagat, MJ Wells, KD Harris, M Carandini, CP Burgess
Eneuro 7 (4), 2020
AlignNet: Unsupervised Entity Alignment
A Creswell, K Nikiforou, O Vinyals, A Saraiva, R Kabra, L Matthey, ...
arXiv preprint arXiv:2007.08973, 2020
Temporal neuronal oscillations can produce spatial phase codes
C Burgess, NW Schuck, N Burgess
Space, Time and Number in the Brain, 59-69, 2011
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