| 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 | 5110 | 2017 |
| Understanding disentangling in -VAE CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ... arXiv preprint arXiv:1804.03599, 2018 | 1136 | 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 | 827 | 2021 |
| Monet: Unsupervised scene decomposition and representation CP Burgess, L Matthey, N Watters, R Kabra, I Higgins, M Botvinick, ... arXiv preprint arXiv:1901.11390, 2019 | 516 | 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 | 507 | 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 | 498 | 2017 |
| dSprites - Disentanglement testing Sprites dataset L Matthey, I Higgins, D Hassabis, A Lercher https://github.com/deepmind/dsprites-dataset, 2017 | 407 | 2017 |
| Selection-inference: Exploiting large language models for interpretable logical reasoning A Creswell, M Shanahan, I Higgins arXiv preprint arXiv:2205.09712, 2022 | 232 | 2022 |
| Hamiltonian generative networks P Toth, DJ Rezende, A Jaegle, S Racanière, A Botev, I Higgins arXiv preprint arXiv:1909.13789, 2019 | 219 | 2019 |
| International Conference on Learning Representations I Higgins, L Matthey, A Pal, C Burgess, X Glorot, M Botvinick, S Mohamed, ... ICLR 2017, Toulon, France, 2017 | 166 | 2017 |
| Scan: Learning hierarchical compositional visual concepts I Higgins, N Sonnerat, L Matthey, A Pal, CP Burgess, M Bosnjak, ... arXiv preprint arXiv:1707.03389, 2017 | 162 | 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 | 139 | 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 | 128 | 2021 |
| 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 | 93 | 2022 |
| Cyprien de Masson d’Autume JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ... | 85 | 2021 |
| 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 | 80 | 2019 |
| Equivariant hamiltonian flows DJ Rezende, S Racanière, I Higgins, P Toth arXiv preprint arXiv:1909.13739, 2019 | 61 | 2019 |
| Cyprien de Masson d’Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew J JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, HF Song, J Aslanides, ... Johnson, Blake A. Hechtman, Laura Weidinger, Iason Gabriel, William S. Isaac …, 2021 | 52 | 2021 |
| Understanding disentangling in β CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ... arXiv preprint arXiv:1804.03599, 2018 | 51 | 2018 |
| Symmetry-based representations for artificial and biological general intelligence I Higgins, S Racanière, D Rezende Frontiers in Computational Neuroscience 16, 836498, 2022 | 43 | 2022 |
Loic MattheyDeepMind在 google.com 的电子邮件经过验证
