| Scalable sparse subspace clustering by orthogonal matching pursuit C You, D Robinson, R Vidal Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 431 | 2016 |
| Oracle based active set algorithm for scalable elastic net subspace clustering C You, CG Li, DP Robinson, R Vidal Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 287 | 2016 |
| Structured sparse subspace clustering: A joint affinity learning and subspace clustering framework CG Li, C You, R Vidal IEEE Transactions on Image Processing 26 (6), 2988-3001, 2017 | 225 | 2017 |
| Rethinking bias-variance trade-off for generalization of neural networks Z Yang, Y Yu, C You, J Steinhardt, Y Ma International Conference on Machine Learning, 10767-10777, 2020 | 204 | 2020 |
| Self-supervised convolutional subspace clustering network J Zhang, CG Li, C You, X Qi, H Zhang, J Guo, Z Lin Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 177 | 2019 |
| Learning diverse and discriminative representations via the principle of maximal coding rate reduction Y Yu, KHR Chan, C You, C Song, Y Ma Advances in neural information processing systems 33, 9422-9434, 2020 | 176 | 2020 |
| A geometric analysis of neural collapse with unconstrained features Z Zhu, T Ding, J Zhou, X Li, C You, J Sulam, Q Qu Advances in Neural Information Processing Systems 34, 29820-29834, 2021 | 150 | 2021 |
| Provable self-representation based outlier detection in a union of subspaces C You, DP Robinson, R Vidal Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 133 | 2017 |
| ReduNet: A white-box deep network from the principle of maximizing rate reduction KHR Chan, Y Yu, C You, H Qi, J Wright, Y Ma Journal of machine learning research 23 (114), 1-103, 2022 | 123* | 2022 |
| Robust training under label noise by over-parameterization S Liu, Z Zhu, Q Qu, C You International Conference on Machine Learning, 14153-14172, 2022 | 98 | 2022 |
| Scalable exemplar-based subspace clustering on class-imbalanced data C You, C Li, DP Robinson, R Vidal Proceedings of the European Conference on Computer Vision (ECCV), 67-83, 2018 | 94 | 2018 |
| On the optimization landscape of neural collapse under mse loss: Global optimality with unconstrained features J Zhou, X Li, T Ding, C You, Q Qu, Z Zhu International Conference on Machine Learning, 27179-27202, 2022 | 79 | 2022 |
| Stochastic sparse subspace clustering Y Chen, CG Li, C You Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 72 | 2020 |
| The lazy neuron phenomenon: On emergence of activation sparsity in transformers Z Li, C You, S Bhojanapalli, D Li, AS Rawat, SJ Reddi, K Ye, F Chern, ... arXiv preprint arXiv:2210.06313, 2022 | 68* | 2022 |
| Geometric Conditions for Subspace-Sparse Recovery C You, R Vidal Proceedings of the 32nd International Conference on Machine Learning (ICML-15), 2015 | 59 | 2015 |
| Learning a self-expressive network for subspace clustering S Zhang, C You, R Vidal, CG Li Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 57 | 2021 |
| Deep isometric learning for visual recognition H Qi, C You, X Wang, Y Ma, J Malik International conference on machine learning, 7824-7835, 2020 | 55 | 2020 |
| Are all losses created equal: A neural collapse perspective J Zhou, C You, X Li, K Liu, S Liu, Q Qu, Z Zhu Advances in Neural Information Processing Systems 35, 31697-31710, 2022 | 45 | 2022 |
| On geometric analysis of affine sparse subspace clustering CG Li, C You, R Vidal IEEE Journal of Selected Topics in Signal Processing 12 (6), 1520-1533, 2018 | 39 | 2018 |
| Incremental learning via rate reduction Z Wu, C Baek, C You, Y Ma Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 34 | 2021 |
Rene VidalRachleff University Professor, University of Pennsylvania; Amazon Scholar; Chief Scientist, NORCE在 seas.upenn.edu 的电子邮件经过验证
