| A Geometric Analysis of Neural Collapse with Unconstrained Features Z Zhu, T Ding, J Zhou, X Li, C You, J Sulam, Q Qu Neural Information Processing Systems (NeurIPS), 2021 | 136 | 2021 |
| Cdfi: Compression-driven network design for frame interpolation T Ding, L Liang, Z Zhu, I Zharkov Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 88 | 2021 |
| Only train once: A one-shot neural network training and pruning framework T Chen, B Ji, T Ding, B Fang, G Wang, Z Zhu, L Liang, Y Shi, S Yi, X Tu Advances in Neural Information Processing Systems 34, 19637-19651, 2021 | 81 | 2021 |
| 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 (ICML), 27179-27202, 2022 | 69 | 2022 |
| Rstt: Real-time spatial temporal transformer for space-time video super-resolution Z Geng, L Liang, T Ding, I Zharkov Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 57 | 2022 |
| A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning Z Zhu, T Ding, DP Robinson, MC Tsakiris, R Vidal Neural Information Processing Systems (NeurIPS), 9437-9447, 2019 | 27 | 2019 |
| Noisy Dual Principal Component Pursuit T Ding, Z Zhu, T Ding, Y Yang, R Vidal, M Tsakiris, D Robinson International Conference on Machine Learning (ICML), 1617-1625, 2019 | 25 | 2019 |
| OTOv2: Automatic, Generic, User-Friendly T Chen, L Liang, T Ding, Z Zhu, I Zharkov International Conference on Learning Representations (ICLR), 2023 | 24 | 2023 |
| Orthant Based Proximal Stochastic Gradient Method for L1-Regularized Optimization T Chen, T Ding, B Ji, G Wang, Y Shi, J Tian, S Yi, X Tu, Z Zhu European Conference on Machine Learning and Principles and Practice of …, 2020 | 23* | 2020 |
| Neural network compression via sparse optimization T Chen, B Ji, Y Shi, T Ding, B Fang, S Yi, X Tu arXiv preprint arXiv:2011.04868, 2020 | 16 | 2020 |
| Dual Principal Component Pursuit for Learning a Union of Hyperplanes: Theory and Algorithms T Ding, Z Zhu, M Tsakiris, R Vidal, D Robinson International Conference on Artificial Intelligence and Statistics (AISTATS …, 2021 | 10 | 2021 |
| Half-space proximal stochastic gradient method for group-sparsity regularized problem T Chen, G Wang, T Ding, B Ji, S Yi, Z Zhu arXiv preprint arXiv:2009.12078, 2020 | 10* | 2020 |
| Lorashear: Efficient large language model structured pruning and knowledge recovery T Chen, T Ding, B Yadav, I Zharkov, L Liang arXiv preprint arXiv:2310.18356, 2023 | 8 | 2023 |
| Dual Principal Component Pursuit for Robust Subspace Learning: Theory and Algorithms for a Holistic Approach T Ding, Z Zhu, R Vidal, DP Robinson International Conference on Machine Learning (ICML), 2021 | 7 | 2021 |
| The efficiency spectrum of large language models: An algorithmic survey T Ding, T Chen, H Zhu, J Jiang, Y Zhong, J Zhou, G Wang, Z Zhu, ... arXiv preprint arXiv:2312.00678, 2023 | 6 | 2023 |
| Sparsity-guided Network Design for Frame Interpolation T Ding, L Liang, Z Zhu, T Chen, I Zharkov arXiv preprint arXiv:2209.04551, 2022 | 6 | 2022 |
| Where and How: Mitigating Confusion in Neural Radiance Fields from Sparse Inputs Y Bao, Y Li, J Huo, T Ding, X Liang, W Li, Y Gao Proceedings of the 31st ACM International Conference on Multimedia (ACMMM), 2023 | 4 | 2023 |
| Towards automatic neural architecture search within general super-networks T Chen, L Liang, T Ding, I Zharkov arXiv preprint arXiv:2305.18030, 2023 | 4 | 2023 |
| DREAM: Diffusion Rectification and Estimation-Adaptive Models J Zhou, T Ding, T Chen, J Jiang, I Zharkov, Z Zhu, L Liang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2024 | 3 | 2024 |
| Subspace learning for data arising from a union of subspaces of high relative dimension T Ding Johns Hopkins University, 2021 | 3 | 2021 |
Zhihui ZhuAssistant Professor, Ohio State University確認したメール アドレス: osu.edu
