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ORCIDItay Safran
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- affiliation: Princeton University, NJ, USA
- affiliation: Weizmann Institute of Science, Rehovot, Israel
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2020 – today
- 2024
[c10]Itay Safran, Daniel Reichman, Paul Valiant:
How Many Neurons Does it Take to Approximate the Maximum? SODA 2024: 3156-3183- [i13]Itay Safran, Daniel Reichman, Paul Valiant:
Depth Separations in Neural Networks: Separating the Dimension from the Accuracy. CoRR abs/2402.07248 (2024) - [i12]Guy Smorodinsky, Gal Vardi, Itay Safran:
Provable Privacy Attacks on Trained Shallow Neural Networks. CoRR abs/2410.07632 (2024) - 2023
- [i11]Itay Safran, Daniel Reichman, Paul Valiant:
How Many Neurons Does it Take to Approximate the Maximum? CoRR abs/2307.09212 (2023) - 2022
- [c9]Itay Safran, Jason D. Lee:
Optimization-Based Separations for Neural Networks. COLT 2022: 3-64 - [c8]Itay Safran, Gal Vardi, Jason D. Lee:
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias. NeurIPS 2022 - [i10]Itay Safran, Gal Vardi, Jason D. Lee:
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias. CoRR abs/2205.09072 (2022) - 2021
- [c7]Itay Safran, Gilad Yehudai, Ohad Shamir:
The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks. COLT 2021: 3889-3934 - [c6]Itay Safran, Ohad Shamir:
Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems. NeurIPS 2021: 15151-15161 - [i9]Itay Safran, Ohad Shamir:
Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems. CoRR abs/2106.06880 (2021) - [i8]Itay Safran, Jason D. Lee:
Optimization-Based Separations for Neural Networks. CoRR abs/2112.02393 (2021) - 2020
- [c5]Itay Safran, Ohad Shamir:
How Good is SGD with Random Shuffling? COLT 2020: 3250-3284 - [i7]Itay Safran, Gilad Yehudai, Ohad Shamir:
The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks. CoRR abs/2006.01005 (2020)
2010 – 2019
- 2019
- [c4]Itay Safran, Ronen Eldan, Ohad Shamir:
Depth Separations in Neural Networks: What is Actually Being Separated? COLT 2019: 2664-2666 - [i6]Adi Shamir, Itay Safran, Eyal Ronen, Orr Dunkelman:
A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance. CoRR abs/1901.10861 (2019) - [i5]Itay Safran, Ronen Eldan, Ohad Shamir:
Depth Separations in Neural Networks: What is Actually Being Separated? CoRR abs/1904.06984 (2019) - [i4]Itay Safran, Ohad Shamir:
How Good is SGD with Random Shuffling? CoRR abs/1908.00045 (2019) - 2018
- [c3]Itay Safran, Ohad Shamir:
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks. ICML 2018: 4430-4438 - 2017
- [c2]Itay Safran, Ohad Shamir:
Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks. ICML 2017: 2979-2987 - [i3]Itay Safran, Ohad Shamir:
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks. CoRR abs/1712.08968 (2017) - 2016
- [c1]Itay Safran, Ohad Shamir:
On the Quality of the Initial Basin in Overspecified Neural Networks. ICML 2016: 774-782 - [i2]Itay Safran, Ohad Shamir:
Depth Separation in ReLU Networks for Approximating Smooth Non-Linear Functions. CoRR abs/1610.09887 (2016) - 2015
- [i1]Itay Safran, Ohad Shamir:
On the Quality of the Initial Basin in Overspecified Neural Networks. CoRR abs/1511.04210 (2015)
Coauthor Index
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