| β-VAE: Learning basic visual concepts with a constrained variational framework I Higgins, L Matthey, A Pal, C Burgess, X Glorot, M Botvinick, S Mohamed, ... | 5015 | 2016 |
| Understanding disentangling in β-VAE CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ... arXiv preprint arXiv:1804.03599, 2018 | 1152 | 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 | 516 | 2019 |
| DARLA: Improving zero-shot transfer in reinforcement learning I Higgins, A Pal, AA Rusu, L Matthey, CP Burgess, A Pritzel, M Botvinick, ... arXiv preprint arXiv:1707.08475, 2017 | 499 | 2017 |
| 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 | 478 | 2019 |
| A Heuristic for Unsupervised Model Selection for Variational Disentangled Representation Learning. S Duan, N Watters, L Matthey, CP Burgess, A Lerchner, I Higgins | 217* | 2019 |
| High-yield methods for accurate two-alternative visual psychophysics in head-fixed mice CP Burgess, A Lak, NA Steinmetz, P Zatka-Haas, CB Reddy, EAK Jacobs, ... Cell Reports 20 (10), 2513-2524, 2017 | 171 | 2017 |
| 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 | 162 | 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 | 143 | 2019 |
| Life-long disentangled representation learning with cross-domain latent homologies A Achille, T Eccles, L Matthey, CP Burgess, N Watters, A Lerchner, ... arXiv preprint arXiv:1808.06508, 2018 | 134 | 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 | 122 | 2019 |
| 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 |
| SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition R Kabra, D Zoran, G Erdogan, L Matthey, A Creswell, M Botvinick, ... arXiv preprint arXiv:2106.03849, 2021 | 71 | 2021 |
| Cortical state fluctuations across layers of V1 during visual spatial perception A Speed, J Del Rosario, CP Burgess, B Haider Cell reports 26 (11), 2868-2874. e3, 2019 | 51 | 2019 |
| Controlling phase noise in oscillatory interference models of grid cell firing CP Burgess, N Burgess Journal of Neuroscience 34 (18), 6224-6232, 2014 | 30 | 2014 |
| The multi-entity variational autoencoder C Nash, SMA Eslami, C Burgess, I Higgins, D Zoran, T Weber, P Battaglia NIPS Workshops, 2017 | 26 | 2017 |
| Unsupervised Object-Based Transition Models for 3D Partially Observable Environments A Creswell, R Kabra, C Burgess, M Shanahan arXiv preprint arXiv:2103.04693, 2021 | 25 | 2021 |
| Rigbox: an Open-Source toolbox for probing neurons and behavior J Bhagat, MJ Wells, KD Harris, M Carandini, CP Burgess Eneuro 7 (4), 2020 | 21 | 2020 |
| AlignNet: Unsupervised Entity Alignment A Creswell, K Nikiforou, O Vinyals, A Saraiva, R Kabra, L Matthey, ... arXiv preprint arXiv:2007.08973, 2020 | 12 | 2020 |
| Temporal neuronal oscillations can produce spatial phase codes C Burgess, NW Schuck, N Burgess Space, Time and Number in the Brain, 59-69, 2011 | 11 | 2011 |
