Mor Shpigel Nacson

According to our database, Mor Shpigel Nacson authored at least 13 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers.

Gon Buzaglo, Itamar Harel, Mor Shpigel Nacson, Alon Brutzkus, Nathan Srebro, Daniel Soudry

CoRR, 2024

2023

Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond.

Itai Kreisler, Mor Shpigel Nacson, Daniel Soudry, Yair Carmon

Proceedings of the International Conference on Machine Learning, 2023

The Implicit Bias of Minima Stability in Multivariate Shallow ReLU Networks.

Mor Shpigel Nacson, Rotem Mulayoff, Greg Ongie, Tomer Michaeli, Daniel Soudry

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Implicit Bias of the Step Size in Linear Diagonal Neural Networks.

Mor Shpigel Nacson, Kavya Ravichandran, Nathan Srebro, Daniel Soudry

Proceedings of the International Conference on Machine Learning, 2022

2021

On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent.

Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E. Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry

Proceedings of the 38th International Conference on Machine Learning, 2021

TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition.

Rami Ben-Ari, Mor Shpigel Nacson, Ophir Azulai, Udi Barzelay, Daniel Rotman

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020

At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks?

Niv Giladi, Mor Shpigel Nacson, Elad Hoffer, Daniel Soudry

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models.

Mor Shpigel Nacson, Suriya Gunasekar, Jason D. Lee, Nathan Srebro, Daniel Soudry

Proceedings of the 36th International Conference on Machine Learning, 2019

Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate.

Mor Shpigel Nacson, Nathan Srebro, Daniel Soudry

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Convergence of Gradient Descent on Separable Data.

Mor Shpigel Nacson, Jason D. Lee, Suriya Gunasekar, Pedro Henrique Pamplona Savarese, Nathan Srebro, Daniel Soudry

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

The Implicit Bias of Gradient Descent on Separable Data.

Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, Nathan Srebro

J. Mach. Learn. Res., 2018

Convergence of Gradient Descent on Separable Data.

Mor Shpigel Nacson, Jason D. Lee, Suriya Gunasekar, Nathan Srebro, Daniel Soudry

CoRR, 2018

The Implicit Bias of Gradient Descent on Separable Data.

Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Nathan Srebro

Proceedings of the 6th International Conference on Learning Representations, 2018