Obserwuj
Nicholas Watters
Nicholas Watters
Zweryfikowany adres z mit.edu
Tytuł
Cytowane przez
Cytowane przez
Rok
Understanding disentangling in -VAE
CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
8292018
Monet: Unsupervised scene decomposition and representation
CP Burgess, L Matthey, N Watters, R Kabra, I Higgins, M Botvinick, ...
arXiv preprint arXiv:1901.11390, 2019
3342019
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
3262019
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
3202017
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
1042018
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
852019
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
852019
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
472019
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
311804
Understanding disentangling in β
CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
VAE, ArXiv e-prints, 2018
172018
Understanding disentangling in β-VAE. arXiv
CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
162018
Understanding disentangling in β β-VAE
CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
122018
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
102018
Spriteworld: A Flexible, Configurable Reinforcement Learning Environment
N Watters, L Matthey, S Borgeaud, R Kabra, A Lerchner
https://github.com/deepmind/spriteworld, 2019
72019
A heuristic for unsupervised model selection for variational disentangled representation learning
S Duan, N Watters, L Matthey, CP Burgess, A Lerchner, I Higgins
arXiv preprint arXiv:1905.12614, 2019
62019
Neuronal spike train entropy estimation by history clustering
N Watters, GN Reeke
Neural Computation 26 (9), 1840-1872, 2014
62014
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
22021
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
22021
Modeling Human Eye Movements with Neural Networks in a Maze-Solving Task
J Li, N Watters, H Sohn, M Jazayeri
arXiv preprint arXiv:2212.10367, 2022
2022
Making object-level predictions of the future state of a physical system
N Watters, R Pascanu, PW Battaglia, D Zorn, TG Weber
US Patent 11,388,424, 2022
2022
Nie można teraz wykonać tej operacji. Spróbuj ponownie później.
Prace 1–20