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
3850 2017 Understanding disentangling in CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
919 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
417 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
389 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
389 2018 Scaling language models: Methods, analysis & insights from training gopher JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ...
arXiv preprint arXiv:2112.11446, 2021
352 2021 dSprites - Disentanglement testing Sprites dataset L Matthey, I Higgins, D Hassabis, A Lercher
https://github.com/deepmind/dsprites-dataset, 2017
311 2017 Hamiltonian generative networks P Toth, DJ Rezende, A Jaegle, S Racanière, A Botev, I Higgins
arXiv preprint arXiv:1909.13789, 2019
170 2019 Scan: Learning hierarchical compositional visual concepts I Higgins, N Sonnerat, L Matthey, A Pal, CP Burgess, M Bosnjak, ...
arXiv preprint arXiv:1707.03389, 2017
147 2017 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
117 2018 Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons I Higgins, L Chang, V Langston, D Hassabis, C Summerfield, D Tsao, ...
Nature communications 12 (1), 6456, 2021
74 2021 Selection-inference: Exploiting large language models for interpretable logical reasoning A Creswell, M Shanahan, I Higgins
arXiv preprint arXiv:2205.09712, 2022
64 2022 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
59 2019 Equivariant hamiltonian flows DJ Rezende, S Racanière, I Higgins, P Toth
arXiv preprint arXiv:1909.13739, 2019
50 2019 The Multi-Entity Variational Autoencoder C Nash, A Eslami, CP Burgess, I Higgins, D Zoran, W Theophane, ...
http://charlienash.github.io/assets/docs/mevae2017.pdf, 2017
24 2017 Disentangling by subspace diffusion D Pfau, I Higgins, A Botev, S Racanière
Advances in Neural Information Processing Systems 33, 17403-17415, 2020
21 2020 Symmetry-based representations for artificial and biological general intelligence I Higgins, S Racanière, D Rezende
Frontiers in Computational Neuroscience, 28, 2022
19 2022 Disentangled cumulants help successor representations transfer to new tasks C Grimm, I Higgins, A Barreto, D Teplyashin, M Wulfmeier, T Hertweck, ...
arXiv preprint arXiv:1911.10866, 2019
18 2019 Which priors matter? Benchmarking models for learning latent dynamics A Botev, A Jaegle, P Wirnsberger, D Hennes, I Higgins
arXiv preprint arXiv:2111.05458, 2021
16 2021 Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain I Higgins, S Stringer, J Schnupp
PLoS One 12 (8), e0180174, 2017
15 2017 
Loic MattheyDeepMindПотвърден имейл адрес: google.com
