Παρακολούθηση
Itay Safran
Itay Safran
Post-Doctoral Research Associate, Purdue University
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα purdue.edu - Αρχική σελίδα
Τίτλος
Παρατίθεται από
Παρατίθεται από
Έτος
Spurious local minima are common in two-layer relu neural networks
I Safran, O Shamir
International conference on machine learning, 4433-4441, 2018
2882018
Depth-width tradeoffs in approximating natural functions with neural networks
I Safran, O Shamir
International conference on machine learning, 2979-2987, 2017
220*2017
On the quality of the initial basin in overspecified neural networks
I Safran, O Shamir
International Conference on Machine Learning, 774-782, 2016
1392016
A simple explanation for the existence of adversarial examples with small hamming distance
A Shamir, I Safran, E Ronen, O Dunkelman
arXiv preprint arXiv:1901.10861, 2019
882019
How good is SGD with random shuffling?
I Safran, O Shamir
Conference on Learning Theory, 3250-3284, 2020
682020
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
Depth separations in neural networks: what is actually being separated?
I Safran, R Eldan, O Shamir
Conference on Learning Theory, 2664-2666, 2019
372019
On the effective number of linear regions in shallow univariate relu networks: Convergence guarantees and implicit bias
I Safran, G Vardi, JD Lee
Advances in Neural Information Processing Systems 35, 32667-32679, 2022
222022
Random shuffling beats sgd only after many epochs on ill-conditioned problems
I Safran, O Shamir
Advances in Neural Information Processing Systems 34, 15151-15161, 2021
182021
Optimization-based separations for neural networks
I Safran, J Lee
Conference on Learning Theory, 3-64, 2022
152022
How Many Neurons Does it Take to Approximate the Maximum?
I Safran, D Reichman, P Valiant
Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms …, 2024
22024
Depth Separations in Neural Networks: Separating the Dimension from the Accuracy
I Safran, D Reichman, P Valiant
arXiv preprint arXiv:2402.07248, 2024
2024
Towards Theoretical Foundations for Artificial Neural Networks
I Safran
Weizmann Institute of Science, 2020
2020
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