| beta-vae: Learning basic visual concepts with a constrained variational framework. I Higgins, L Matthey, A Pal, CP Burgess, X Glorot, MM Botvinick, ... ICLR (Poster) 3, 2017 | 5864 | 2017 |
| Understanding disentangling in -VAE CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ... arXiv preprint arXiv:1804.03599, 2018 | 1267 | 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 | 1220* | 2021 |
| Towards a definition of disentangled representations I Higgins, D Amos, D Pfau, S Racaniere, L Matthey, D Rezende, ... arXiv preprint arXiv:1812.02230, 2018 | 576 | 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 | 554 | 2019 |
| 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 | 527 | 2017 |
| dSprites - Disentanglement testing Sprites dataset L Matthey, I Higgins, D Hassabis, A Lercher https://github.com/deepmind/dsprites-dataset, 2017 | 450 | 2017 |
| Selection-inference: Exploiting large language models for interpretable logical reasoning A Creswell, M Shanahan, I Higgins arXiv preprint arXiv:2205.09712, 2022 | 313 | 2022 |
| Hamiltonian generative networks P Toth, DJ Rezende, A Jaegle, S Racanière, A Botev, I Higgins arXiv preprint arXiv:1909.13789, 2019 | 249 | 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 | 171 | 2017 |
| Solving math word problems with process-and outcome-based feedback J Uesato, N Kushman, R Kumar, F Song, N Siegel, L Wang, A Creswell, ... arXiv preprint arXiv:2211.14275, 2022 | 156 | 2022 |
| 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 | 150 | 2021 |
| 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 | 140 | 2018 |
| 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 | 85 | 2019 |
| Equivariant hamiltonian flows DJ Rezende, S Racanière, I Higgins, P Toth arXiv preprint arXiv:1909.13739, 2019 | 70 | 2019 |
| Symmetry-based representations for artificial and biological general intelligence I Higgins, S Racanière, D Rezende Frontiers in Computational Neuroscience 16, 836498, 2022 | 45 | 2022 |
| Disentangling by subspace diffusion D Pfau, I Higgins, A Botev, S Racanière Advances in Neural Information Processing Systems 33, 17403-17415, 2020 | 35 | 2020 |
| 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 | 28 | 2021 |
| 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 | 26 | 2017 |
| Generalizing universal function approximators I Higgins Nature Machine Intelligence 3 (3), 192-193, 2021 | 23 | 2021 |
Loic MattheyDeepMind在 google.com 的电子邮件经过验证
