Folgen
Tom Goldstein
Tom Goldstein
Volpi-Cupal Professor of Computer Science, University of Maryland
Bestätigte E-Mail-Adresse bei cs.umd.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
The split Bregman method for L1-regularized problems
T Goldstein, S Osher
SIAM journal on imaging sciences 2 (2), 323-343, 2009
53372009
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
23092018
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
16222019
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
13182018
Fast alternating direction optimization methods
T Goldstein, B O'Donoghue, S Setzer, R Baraniuk
SIAM Journal on Imaging Sciences 7 (3), 1588-1623, 2014
9632014
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
6302023
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
5982010
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
5482019
Certified data removal from machine learning models
C Guo, T Goldstein, A Hannun, L Van Der Maaten
arXiv preprint arXiv:1911.03030, 2019
4662019
Are adversarial examples inevitable?
A Shafahi, WR Huang, C Studer, S Feizi, T Goldstein
International Conference on Learning Representations, 2019
3732019
Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses
M Goldblum, D Tsipras, C Xie, X Chen, A Schwarzschild, D Song, ...
IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (2), 1563-1580, 2022
372*2022
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
3662017
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
3632019
Saint: Improved neural networks for tabular data via row attention and contrastive pre-training
G Somepalli, M Goldblum, A Schwarzschild, CB Bruss, T Goldstein
arXiv preprint arXiv:2106.01342, 2021
349*2021
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
3292016
Convex phase retrieval without lifting via PhaseMax
T Goldstein, C Studer
International Conference on Machine Learning, 1273-1281, 2017
323*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
3202020
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
3152018
A field guide to forward-backward splitting with a FASTA implementation
T Goldstein, C Studer, R Baraniuk
arXiv preprint arXiv:1411.3406, 2014
308*2014
Baseline defenses for adversarial attacks against aligned language models
N Jain, A Schwarzschild, Y Wen, G Somepalli, J Kirchenbauer, P Chiang, ...
arXiv preprint arXiv:2309.00614, 2023
298*2023
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–20