| 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 | 43 | 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 | 17 | 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 | 17* | |
| 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 | 13 | 2023 |
| 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 | 11 | 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 | 9 | 2022 |
| Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation C Yaras, P Wang, L Balzano, Q Qu arXiv preprint arXiv:2406.04112, 2024 | 7 | 2024 |
| Invariant Low-Dimensional Subspaces in Gradient Descent for Learning Deep Matrix Factorizations C Yaras, P Wang, W Hu, Z Zhu, L Balzano, Q Qu NeurIPS 2023 Workshop on Mathematics of Modern Machine Learning, 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 | 3 | 2023 |
Qing QuAssistant Professor, Dept. of EECS, University of MichiganEmail verificata su umich.edu