| 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 | 5770 | 2017 |
| Understanding disentangling in -VAE CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ... arXiv preprint arXiv:1804.03599, 2018 | 1299 | 2018 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 989 | 2024 |
| Towards a definition of disentangled representations I Higgins, D Amos, D Pfau, S Racaniere, L Matthey, D Rezende, ... arXiv preprint arXiv:1812.02230, 2018 | 594 | 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 | 566 | 2019 |
| Multi-Object Representation Learning with Iterative Variational Inference K Greff, RL Kaufmann, R Kabra, N Watters, CP Burgess, Z Daniel, ... arXiv preprint arXiv:1903.00450, 2019 | 541 | 2019 |
| DARLA: Improving zero-shot transfer in reinforcement learning I Higgins, A Pal, A Rusu, L Matthey, C Burgess, A Pritzel, M Botvinick, ... Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 540 | 2017 |
| dSprites: Disentanglement testing Sprites dataset L Matthey, I Higgins, D Hassabis, A Lerchner https://github.com/deepmind/dsprites-dataset, 2017 | 466 | 2017 |
| 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 | 260* | 2016 |
| International Conference on Learning Representations I Higgins, L Matthey, A Pal, C Burgess, X Glorot, M Botvinick, S Mohamed, ... ICLR 2017, Toulon, France, 2017 | 200 | 2017 |
| 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 | 181 | 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 | 174 | 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 | 142 | 2018 |
| 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 | 129 | 2019 |
| A comparison of casting and spiraling algorithms for odor source localization in laminar flow T Lochmatter, X Raemy, L Matthey, S Indra, A Martinoli 2008 IEEE International Conference on Robotics and Automation, 1138-1143, 2008 | 96 | 2008 |
| 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 | 89 | 2019 |
| Stochastic strategies for a swarm robotic assembly system L Matthey, S Berman, V Kumar 2009 IEEE International Conference on Robotics and Automation, 1953-1958, 2009 | 87 | 2009 |
| 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 | 80 | 2021 |
| Multi-object datasets R Kabra, C Burgess, L Matthey, RL Kaufman, K Greff, M Reynolds, ... DeepMind 5 (6), 7, 2019 | 80 | 2019 |
| Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents JX Wang, M King, N Porcel, Z Kurth-Nelson, T Zhu, C Deck, P Choy, ... arXiv preprint arXiv:2102.02926, 2021 | 63* | 2021 |
Alexander LerchnerSenior Staff Scientist, Google DeepMindVerified email at google.com