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Gilad Yehudai
Gilad Yehudai
Postdoctoral Associate, New York University
Verified email at weizmann.ac.il - Homepage
Title
Cited by
Cited by
Year
Proving the lottery ticket hypothesis: Pruning is all you need
E Malach, G Yehudai, S Shalev-Schwartz, O Shamir
International Conference on Machine Learning, 6682-6691, 2020
2672020
On the power and limitations of random features for understanding neural networks
G Yehudai, O Shamir
Advances in Neural Information Processing Systems, 2019
1942019
From Local Structures to Size Generalization in Graph Neural Networks
G Yehudai, E Fetaya, E Meirom, G Chechik, H Maron
arXiv preprint arXiv:2010.08853, 2020
1032020
Reconstructing training data from trained neural networks
N Haim, G Vardi, G Yehudai, O Shamir, M Irani
Advances in Neural Information Processing Systems 35, 22911-22924, 2022
922022
Learning a single neuron with gradient methods
G Yehudai, S Ohad
Conference on Learning Theory, 3756-3786, 2020
712020
The effects of mild over-parameterization on the optimization landscape of shallow relu neural networks
IM Safran, G Yehudai, O Shamir
Conference on Learning Theory, 3889-3934, 2021
372021
Gradient methods provably converge to non-robust networks
G Vardi, G Yehudai, O Shamir
Advances in Neural Information Processing Systems 35, 20921-20932, 2022
212022
The connection between approximation, depth separation and learnability in neural networks
E Malach, G Yehudai, S Shalev-Schwartz, O Shamir
Conference on Learning Theory, 3265-3295, 2021
202021
Learning a single neuron with bias using gradient descent
G Vardi, G Yehudai, O Shamir
Advances in Neural Information Processing Systems 34, 28690-28700, 2021
192021
On the optimal memorization power of relu neural networks
G Vardi, G Yehudai, O Shamir
arXiv preprint arXiv:2110.03187, 2021
182021
Width is less important than depth in ReLU neural networks
G Vardi, G Yehudai, O Shamir
Conference on learning theory, 1249-1281, 2022
112022
From tempered to benign overfitting in relu neural networks
G Kornowski, G Yehudai, O Shamir
Advances in Neural Information Processing Systems 36, 2024
92024
Generating collection rules based on security rules
NA Arbel, L Lazar, G Yehudai
US Patent 11,330,016, 2022
72022
On size generalization in graph neural networks
G Yehudai, E Fetaya, E Meirom, G Chechik, H Maron
42020
Deconstructing data reconstruction: Multiclass, weight decay and general losses
G Buzaglo, N Haim, G Yehudai, G Vardi, Y Oz, Y Nikankin, M Irani
Advances in Neural Information Processing Systems 36, 2024
32024
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces
O Melamed, G Yehudai, G Vardi
Advances in Neural Information Processing Systems 36, 2024
2*2024
RedEx: Beyond Fixed Representation Methods via Convex Optimization
A Daniely, M Schain, G Yehudai
arXiv preprint arXiv:2401.07606, 2024
2024
Locally Optimal Descent for Dynamic Stepsize Scheduling
G Yehudai, A Cohen, A Daniely, Y Drori, T Koren, M Schain
arXiv preprint arXiv:2311.13877, 2023
2023
Reconstructing Training Data from Multiclass Neural Networks
G Buzaglo, N Haim, G Yehudai, G Vardi, M Irani
arXiv preprint arXiv:2305.03350, 2023
2023
Aggregating alerts of malicious events for computer security
G Yehudai, I Mantin, L Fisch, S Hershkovitz, A Shulman, MR Ambar
US Patent 11,218,448, 2022
2022
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