| A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics CG Yan, B Cheung, C Kelly, S Colcombe, RC Craddock, A Di Martino, ... Neuroimage 76, 183-201, 2013 | 1295 | 2013 |
| The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry KB Nooner, S Colcombe, R Tobe, M Mennes, M Benedict, A Moreno, ... Frontiers in Neuroscience 6, 152, 0 | 674 | |
| Adversarial examples that fool both computer vision and time-limited humans G Elsayed, S Shankar, B Cheung, N Papernot, A Kurakin, I Goodfellow, ... Advances in neural information processing systems 31, 2018 | 297 | 2018 |
| Discovering hidden factors of variation in deep networks B Cheung, JA Livezey, AK Bansal, BA Olshausen arXiv preprint arXiv:1412.6583, 2014 | 211 | 2014 |
| Towards automated analysis of connectomes: The configurable pipeline for the analysis of connectomes (c-pac) C Craddock, S Sikka, B Cheung, R Khanuja, SS Ghosh, C Yan, Q Li, ... Front Neuroinform 42, 10-3389, 2013 | 197 | 2013 |
| Meta-learning update rules for unsupervised representation learning L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein arXiv preprint arXiv:1804.00222, 2018 | 95 | 2018 |
| Superposition of many models into one B Cheung, A Terekhov, Y Chen, P Agrawal, B Olshausen Advances in neural information processing systems 32, 2019 | 90 | 2019 |
| Convolutional neural networks applied to human face classification B Cheung 2012 11th International Conference on Machine Learning and Applications 2 …, 2012 | 47 | 2012 |
| Emergence of foveal image sampling from learning to attend in visual scenes B Cheung, E Weiss, B Olshausen arXiv preprint arXiv:1611.09430, 2016 | 40 | 2016 |
| Learning unsupervised learning rules L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein International Conference on Learning Representations, 2019 | 39 | 2019 |
| Cautious adaptation for reinforcement learning in safety-critical settings J Zhang, B Cheung, C Finn, S Levine, D Jayaraman International Conference on Machine Learning, 11055-11065, 2020 | 36 | 2020 |
| Towards automated analysis of connectomes: The configurable pipeline for the analysis of connectomes (c-pac) S Sikka, B Cheung, R Khanuja, S Ghosh, C Yan, Q Li, J Vogelstein, ... 5th INCF Congress of Neuroinformatics, Munich, Germany 10, 2014 | 33 | 2014 |
| The low-rank simplicity bias in deep networks M Huh, H Mobahi, R Zhang, B Cheung, P Agrawal, P Isola arXiv preprint arXiv:2103.10427, 2021 | 30 | 2021 |
| The NKI-Rockland sample: a model for accelerating the pace of discovery science in psychiatry. Front. Neurosci. 6, 152 KB Nooner, SJ Colcombe, RH Tobe, M Mennes, MM Benedict, AL Moreno, ... | 28 | 2012 |
| Equivariant contrastive learning R Dangovski, L Jing, C Loh, S Han, A Srivastava, B Cheung, P Agrawal, ... arXiv preprint arXiv:2111.00899, 2021 | 23 | 2021 |
| Hardware-Optimized Ziggurat Algorithm for High-Speed Gaussian Random Number Generators HM Edrees, B Cheung, MC Sandora, D Nummey, D Stefan International Conference on Engineering of Reconfigurable Systems …, 2009 | 21 | 2009 |
| Hybrid evolution of convolutional networks B Cheung, C Sable 2011 10th international conference on machine learning and applications and …, 2011 | 20 | 2011 |
| Detecting stable individual differences in the functional organization of the human basal ganglia M Garcia-Garcia, A Nikolaidis, P Bellec, RC Craddock, B Cheung, ... NeuroImage 170, 68-82, 2018 | 18 | 2018 |
| A neural architecture for representing and reasoning about spatial relationships E Weiss, B Cheung, B Olshausen | 17 | 2016 |
| Equivariant self-supervised learning: Encouraging equivariance in representations R Dangovski, L Jing, C Loh, S Han, A Srivastava, B Cheung, P Agrawal, ... International Conference on Learning Representations, 2022 | 15 | 2022 |
