Tom Goldstein
Tom Goldstein
Volpi-Cupal Professor of Computer Science, University of Maryland
Verified email at - Homepage
Cited by
Cited by
The split Bregman method for L1-regularized problems
T Goldstein, S Osher
SIAM journal on imaging sciences 2 (2), 323-343, 2009
Visualizing the loss landscape of neural nets
H Li, Z Xu, G Taylor, C Studer, T Goldstein
Advances in neural information processing systems 31, 2018
Adversarial training for free!
A Shafahi, M Najibi, MA Ghiasi, Z Xu, J Dickerson, C Studer, LS Davis, ...
Advances in neural information processing systems 32, 2019
Poison frogs! targeted clean-label poisoning attacks on neural networks
A Shafahi, WR Huang, M Najibi, O Suciu, C Studer, T Dumitras, ...
Advances in neural information processing systems 31, 2018
Fast alternating direction optimization methods
T Goldstein, B O'Donoghue, S Setzer, R Baraniuk
SIAM Journal on Imaging Sciences 7 (3), 1588-1623, 2014
Geometric applications of the split Bregman method: segmentation and surface reconstruction
T Goldstein, X Bresson, S Osher
Journal of scientific computing 45, 272-293, 2010
Freelb: Enhanced adversarial training for natural language understanding
C Zhu, Y Cheng, Z Gan, S Sun, T Goldstein, J Liu
arXiv preprint arXiv:1909.11764, 2019
A watermark for large language models
J Kirchenbauer, J Geiping, Y Wen, J Katz, I Miers, T Goldstein
International Conference on Machine Learning, 17061-17084, 2023
Quantized precoding for massive MU-MIMO
S Jacobsson, G Durisi, M Coldrey, T Goldstein, C Studer
IEEE Transactions on Communications 65 (11), 4670-4684, 2017
Certified data removal from machine learning models
C Guo, T Goldstein, A Hannun, L Van Der Maaten
arXiv preprint arXiv:1911.03030, 2019
Are adversarial examples inevitable?
A Shafahi, WR Huang, C Studer, S Feizi, T Goldstein
International Conference on Learning Representations, 2019
Transferable clean-label poisoning attacks on deep neural nets
C Zhu, WR Huang, H Li, G Taylor, C Studer, T Goldstein
International conference on machine learning, 7614-7623, 2019
Training neural networks without gradients: A scalable admm approach
G Taylor, R Burmeister, Z Xu, B Singh, A Patel, T Goldstein
International conference on machine learning, 2722-2731, 2016
Convex phase retrieval without lifting via PhaseMax
T Goldstein, C Studer
International Conference on Machine Learning, 1273-1281, 2017
Dcan: Dual channel-wise alignment networks for unsupervised scene adaptation
Z Wu, X Han, YL Lin, MG Uzunbas, T Goldstein, SN Lim, LS Davis
Proceedings of the European Conference on Computer Vision (ECCV), 518-534, 2018
A field guide to forward-backward splitting with a FASTA implementation
T Goldstein, C Studer, R Baraniuk
arXiv preprint arXiv:1411.3406, 2014
Splitting methods in communication, imaging, science, and engineering
R Glowinski, SJ Osher, W Yin
Springer, 2017
Making an invisibility cloak: Real world adversarial attacks on object detectors
Z Wu, SN Lim, LS Davis, T Goldstein
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020
Training quantized nets: A deeper understanding
H Li, S De, Z Xu, C Studer, H Samet, T Goldstein
Advances in Neural Information Processing Systems 30, 2017
The intrinsic dimension of images and its impact on learning
P Pope, C Zhu, A Abdelkader, M Goldblum, T Goldstein
arXiv preprint arXiv:2104.08894, 2021
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