Tianjiao Ding

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Yi Ma (马毅)Director of School of Computing and Data Science, HKU; Professor of EECS, UC BerkeleyVerified email at eecs.berkeley.edu
Rene VidalRachleff University Professor, University of Pennsylvania; Amazon Scholar; Chief Scientist, NORCEVerified email at seas.upenn.edu
Ben HaeffeleJohns Hopkins UnviersityVerified email at jhu.edu
Daniel P. RobinsonAssociate Professor, Lehigh UniversityVerified email at lehigh.edu
Kwan Ho Ryan ChanUniversity of PennsylvaniaVerified email at seas.upenn.edu
Shengbang TongNYU CourantVerified email at berkeley.edu
Laurent KneipProfessor at ShanghaiTech, Director of the Mobile Perception LabVerified email at shanghaitech.edu.cn
Liangzu PengUniversity of PennsylvaniaVerified email at seas.upenn.edu
Chris Callison-BurchProfessor, University of PennsylvaniaVerified email at upenn.edu

Tianjiao Ding

Other names丁天骄

University of Pennsylvania

Verified email at upenn.edu - Homepage

Optimization Data Science Geometric Vision Foundation Models


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Learning to Parse Wireframes in Images of Man-Made Environments K Huang, Y Wang, Z Zhou, T Ding, S Gao, Y Ma IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018	206	2018
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), 2019	24	2019
Robust Homography Estimation via Dual Principal Component Pursuit T Ding, Y Yang, Z Zhu, DP Robinson, R Vidal, L Kneip, MC Tsakiris IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020	21	2020
Efficient Maximal Coding Rate Reduction by Variational Forms C Baek, Z Wu, KHR Chan, T Ding, Y Ma, BD Haeffele IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022	10	2022
Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models T Chu, S Tong, T Ding*, X Dai, BD Haeffele, R Vidal, Y Ma International Conference on Learning Representations (ICLR), 2024	9	2024
Understanding Doubly Stochastic Clustering T Ding, D Lim, R Vidal, BD Haeffele International Conference on Machine Learning (ICML), 2022	8	2022
Unsupervised Manifold Linearizing and Clustering T Ding, S Tong, KHR Chan, X Dai, Y Ma, BD Haeffele IEEE International Conference on Computer Vision (ICCV), 2023	7	2023
PaCE: Parsimonious Concept Engineering for Large Language Models J Luo, T Ding, KHR Chan, D Thaker, A Chattopadhyay, ... arXiv preprint arXiv:2406.04331, 2024		2024
HARD: Hyperplane ARrangement Descent T Ding, L Peng, R Vidal Conference on Parsimony and Learning (CPAL), 2024		2024
Boosting RANSAC via Dual Principal Component Pursuit Y Yang, X Zhang, T Ding, DP Robinson, R Vidal, MC Tsakiris arXiv preprint arXiv:2110.02918, 2021		2021
Outlier-Robust Orthogonal Regression on Manifolds T Ding, L Peng, R Vidal

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