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Tianyu Ding, Ph.D.
Tianyu Ding, Ph.D.
Senior Researcher, Microsoft | Johns Hopkins University
Verified email at microsoft.com - Homepage
Title
Cited by
Cited by
Year
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
1302021
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
842021
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
772021
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
662022
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
562022
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
262019
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
242019
OTOv2: Automatic, Generic, User-Friendly
T Chen, L Liang, T Ding, Z Zhu, I Zharkov
International Conference on Learning Representations (ICLR), 2023
212023
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
21*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
152020
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
92021
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
9*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
62023
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
52023
Sparsity-guided Network Design for Frame Interpolation
T Ding, L Liang, Z Zhu, T Chen, I Zharkov
arXiv preprint arXiv:2209.04551, 2022
52022
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
52021
Towards automatic neural architecture search within general super-networks
T Chen, L Liang, T Ding, I Zharkov
arXiv preprint arXiv:2305.18030, 2023
42023
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
22023
Subspace learning for data arising from a union of subspaces of high relative dimension
T Ding
Johns Hopkins University, 2021
22021
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
12024
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