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Nicholas Watters
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Understanding disentangling in -VAE
CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
7262018
Visual interaction networks: Learning a physics simulator from video
N Watters, D Zoran, T Weber, P Battaglia, R Pascanu, A Tacchetti
Advances in neural information processing systems 30, 2017
3112017
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
2912019
Monet: Unsupervised scene decomposition and representation
CP Burgess, L Matthey, N Watters, R Kabra, I Higgins, M Botvinick, ...
arXiv preprint arXiv:1901.11390, 2019
2862019
Life-long disentangled representation learning with cross-domain latent homologies
A Achille, T Eccles, L Matthey, C Burgess, N Watters, A Lerchner, ...
Advances in Neural Information Processing Systems 31, 2018
992018
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
782019
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
742019
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
372019
Understanding disentangling in β-VAE. arXiv 2018
CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 1804
281804
Understanding disentangling in β-VAE. arXiv
CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
152018
Understanding disentangling in β
CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
VAE, ArXiv e-prints, 2018
152018
Understanding disentangling in SS-VAE
CP Burgess, I Higgins, A Pal, L Matthey, N Watters
arXiv: 1804.03599, 2018
142018
Understanding disentangling in β β-VAE
CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
102018
Understanding disentangling in β-VAE. arXiv e-prints, page
CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
92018
Spriteworld: A Flexible, Configurable Reinforcement Learning Environment
N Watters, L Matthey, S Borgeaud, R Kabra, A Lerchner
https://github.com/deepmind/spriteworld, 2019
72019
Neuronal spike train entropy estimation by history clustering
N Watters, GN Reeke
Neural Computation 26 (9), 1840-1872, 2014
72014
A Heuristic for Unsupervised Model Selection for Variational Disentangled Representation Learning.
S Duan, N Watters, L Matthey, CP Burgess, A Lerchner, I Higgins
62019
Modular object-oriented games: a task framework for reinforcement learning, psychology, and neuroscience
N Watters, J Tenenbaum, M Jazayeri
arXiv preprint arXiv:2102.12616, 2021
12021
Spatial broadcast decoder: A simple architecture for disentangled representations in vaes
N Watters, L Matthey, CP Burgess, A Lerchner
12019
Making object-level predictions of the future state of a physical system
N Watters, R Pascanu, PW Battaglia, D Zorn, TG Weber
US Patent App. 17/137,255, 2021
2021
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Articles 1–20