LEARNING PARITIES WITH NEURAL NETWORKS

von Daniely, Amit
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 25 of 27 (2021)

THE IMPLICATIONS OF LOCAL CORRELATION ON LEARNING SOME DEEP FUNCTIONS

von Malach, Eran
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 2 of 27 (2021)

IS DEEPER BETTER ONLY WHEN SHALLOW IS GOOD?

von Malach, Eran
Veröffentlicht in NeurIPS (33. : 2019 : Vancouver, British Columbia) 32nd Conference on Neural Information Processing Systems (NeurIPS 2019) ; Volume 9 of 20 (2020)

QUANTIFYING THE BENEFIT OF USING DIFFERENTIABLE LEARNING OVER TANGENT KERNELS

von MALACH, ERAN
Veröffentlicht in International Conference on Machine Learning (38. : 2021 : Online) International Conference on Machine Learning (ICML 2021 ; Part 10 of 16 (2022)

Knowledge Distillation: Bad Models Can Be Good Role Models

von Kaplun, Gal
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 37 of 50 (2023)

On the Power of Differentiable Learning versus PAC and SQ Learning

von Abbe, Emmanuel
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 29 of 36 (2022)

Is ML-Based Cryptanalysis Inherently Limited? Simulating Cryptographic Adversaries via Gradient-Based Methods

von Shafran, Avital
Veröffentlicht in Annual International Cryptology Conference (44. : 2024 : Santa Barbara, Calif.) Advances in cryptology - CRYPTO 2024 ; Part 6 (2024)

Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit

von Barak, Boaz
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 28 of 50 (2023)

Efficient Learning of CNNs Using Patch Based Features

von Brutzkus, Alon
Veröffentlicht in International Conference on Machine Learning (39. : 2022 : Baltimore, Md.; Online) International Conference on Machine Learning (ICML 2022) ; Part 3 of 33 (2023)