| Neural collapse with normalized features: A geometric analysis over the riemannian manifold C Yaras, P Wang, Z Zhu, L Balzano, Q Qu Advances in neural information processing systems 35, 11547-11560, 2022 | 30 | 2022 |
| Randomized histogram matching: A simple augmentation for unsupervised domain adaptation in overhead imagery C Yaras, K Kassaw, B Huang, K Bradbury, JM Malof IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 | 13 | 2023 |
| Linear Convergence Analysis of Neural Collapse with Unconstrained Features P Wang, H Liu, C Yaras, L Balzano, Q Qu OPT 2022: Optimization for Machine Learning (NeurIPS 2022 Workshop), 0 | 9* | |
| The law of parsimony in gradient descent for learning deep linear networks C Yaras, P Wang, W Hu, Z Zhu, L Balzano, Q Qu arXiv preprint arXiv:2306.01154, 2023 | 5 | 2023 |
| Miniaturizing a chip-scale spectrometer using local strain engineering and total-variation regularized reconstruction T Sarwar, C Yaras, X Li, Q Qu, PC Ku Nano Letters 22 (20), 8174-8180, 2022 | 5 | 2022 |
| Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination P Wang, X Li, C Yaras, Z Zhu, L Balzano, W Hu, Q Qu arXiv preprint arXiv:2311.02960, 2023 | 3 | 2023 |
| Accelerating Deep Learning in Reconstructive Spectroscopy Using Synthetic Data P Li, C Yaras, T Sarwar, PC Ku, Q Qu CLEO: Applications and Technology, JTu2A. 71, 2023 | 1 | 2023 |
Qing QuAssistant Professor, Dept. of EECS, University of Michiganumich.edu의 이메일 확인됨
