| 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, 2013 | 1550 | 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, 0 | 923* | |
| The platonic representation hypothesis M Huh*, B Cheung*, T Wang*, P Isola* International Conference on Machine Learning, 2024 | 447 | 2024 |
| Adversarial examples that fool both computer vision and time-limited humans G Elsayed, S Shankar, B Cheung, N Papernot, A Kurakin, I Goodfellow, ... Neural Information Processing Systems, 2018 | 434* | 2018 |
| 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, ... Frontiers in Neuroinformatics, 2013 | 391* | 2013 |
| Discovering hidden factors of variation in deep networks B Cheung*, JA Livezey*, AK Bansal, BA Olshausen arXiv preprint arXiv:1412.6583, 2014 | 236 | 2014 |
| Meta-learning update rules for unsupervised representation learning L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein International Conference on Learning Representations, 2018 | 210* | 2018 |
| Equivariant contrastive learning R Dangovski, L Jing, C Loh, S Han, A Srivastava, B Cheung, P Agrawal, ... International Conference on Learning Representations, 2021 | 176 | 2021 |
| 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 | 171 | 2021 |
| Superposition of many models into one B Cheung, A Terekhov, Y Chen, P Agrawal, B Olshausen Neural Information Processing Systems, 2019 | 161 | 2019 |
| Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. 0.13. 1 KJ Gorgolewski, O Esteban, D Gage Ellis, MP Notter, E Ziegler, ... Zenodo, 2017 | 117 | 2017 |
| Straightening out the straight-through estimator: Overcoming optimization challenges in vector quantized networks M Huh, B Cheung, P Agrawal, P Isola International Conference on Machine Learning, 2023 | 92 | 2023 |
| Cautious adaptation for reinforcement learning in safety-critical settings J Zhang, B Cheung, C Finn, S Levine, D Jayaraman International Conference on Machine Learning, 2020 | 88 | 2020 |
| nipy/nipype: 1.5. 1 O Esteban, CJ Markiewicz, C Burns, M Goncalves, D Jarecka, E Ziegler, ... Zenodo, 2020 | 88 | 2020 |
| Emergence of foveal image sampling from learning to attend in visual scenes B Cheung, E Weiss, B Olshausen International Conference on Learning Representations, 2017 | 55 | 2017 |
| Convolutional neural networks applied to human face classification B Cheung International Conference on Machine Learning and Applications, 2012 | 47 | 2012 |
| Subtle adversarial image manipulations influence both human and machine perception V Veerabadran, J Goldman, S Shankar, B Cheung, N Papernot, A Kurakin, ... Nature Communications, 2023 | 29 | 2023 |
| System identification of neural systems: If we got it right, would we know? Y Han, TA Poggio, B Cheung International Conference on Machine Learning, 2023 | 26 | 2023 |
| 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, 2018 | 26 | 2018 |
| Hybrid evolution of convolutional networks B Cheung, C Sable International Conference on Machine Learning and Applications, 2011 | 26 | 2011 |
Cameron CraddockThe Neuro BureauVerified email at meta.com
