Understanding disentangling in CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
1208 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
531 2019 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
493 2019 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
411 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
151 2019 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
138 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
124 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
83 2019 Understanding disentangling in β CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
57 2018 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
40 1804 Understanding disentangling in β-VAE. arXiv CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
22 2018 Spriteworld: A Flexible, Configurable Reinforcement Learning Environment N Watters, L Matthey, S Borgeaud, R Kabra, A Lerchner
https://github.com/deepmind/spriteworld, 2019
19 2019 Understanding disentangling in β β-VAE CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
12 2018 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
11 2018 Spatial broadcast decoder: A simple architecture for disentangled representations in VAEs N Watters, L Matthey, CP Burgess, A Lerchner
9 2019 Neuronal spike train entropy estimation by history clustering N Watters, GN Reeke
Neural Computation 26 (9), 1840-1872, 2014
6 2014 Modeling human eye movements with neural networks in a maze-solving task J Li, N Watters, H Sohn, M Jazayeri
Annual Conference on Neural Information Processing Systems, 98-112, 2023
5 2023 Making object-level predictions of the future state of a physical system N Watters, R Pascanu, PW Battaglia, D Zorn, TG Weber
US Patent 10,887,607, 2021
5 2021 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
4 2021 Computational basis of hierarchical and counterfactual information processing M Ramadan, C Tang, N Watters, M Jazayeri
bioRxiv, 2024.01. 30.578076, 2024
1 2024 