beta-vae: Learning basic visual concepts with a constrained variational framework I Higgins, L Matthey, A Pal, C Burgess, X Glorot, M Botvinick, S Mohamed, ...
International conference on learning representations, 2017
3498 2017 Understanding disentangling in CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
877 2018 Darla: Improving zero-shot transfer in reinforcement learning I Higgins, A Pal, A Rusu, L Matthey, C Burgess, A Pritzel, M Botvinick, ...
International Conference on Machine Learning, 1480-1490, 2017
398 2017 Monet: Unsupervised scene decomposition and representation CP Burgess, L Matthey, N Watters, R Kabra, I Higgins, M Botvinick, ...
arXiv preprint arXiv:1901.11390, 2019
366 2019 Towards a definition of disentangled representations I Higgins, D Amos, D Pfau, S Racaniere, L Matthey, D Rezende, ...
arXiv preprint arXiv:1812.02230, 2018
366 2018 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
358 2019 dsprites: Disentanglement testing sprites dataset L Matthey, I Higgins, D Hassabis, A Lerchner
294 2017 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
176 * 2019 Early visual concept learning with unsupervised deep learning I Higgins, L Matthey, X Glorot, A Pal, B Uria, C Blundell, S Mohamed, ...
arXiv preprint arXiv:1606.05579, 2016
174 2016 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
109 2018 Scan: Learning hierarchical compositional visual concepts I Higgins, N Sonnerat, L Matthey, A Pal, CP Burgess, M Bosnjak, ...
arXiv preprint arXiv:1707.03389, 2017
107 2017 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
94 2019 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
93 2019 Response variability in balanced cortical networks A Lerchner, C Ursta, J Hertz, M Ahmadi, P Ruffiot, S Enemark
Neural computation 18 (3), 634-659, 2006
64 2006 Multi-object datasets R Kabra, C Burgess, L Matthey, RL Kaufman, K Greff, M Reynolds, ...
DeepMind 5 (6), 7, 2019
39 2019 Simone: View-invariant, temporally-abstracted object representations via unsupervised video decomposition R Kabra, D Zoran, G Erdogan, L Matthey, A Creswell, M Botvinick, ...
Advances in Neural Information Processing Systems 34, 20146-20159, 2021
32 2021 Mean field theory for a balanced hypercolumn model of orientation selectivity in primary visual cortex A Lerchner, G Sterner, J Hertz, M Ahmadi
Network: Computation in Neural Systems 17 (2), 131-150, 2006
32 2006 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
29 2017 Parts: Unsupervised segmentation with slots, attention and independence maximization D Zoran, R Kabra, A Lerchner, DJ Rezende
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
25 2021 High-conductance states in a mean-field cortical network model A Lerchner, M Ahmadi, J Hertz
Neurocomputing 58, 935-940, 2004
25 2004 
Loic MattheyDeepMindПотвърден имейл адрес: google.com
