The Connection Between Approximation, Depth Separation and Learnability in Neural Networks

31 January 2021

Papers citing "The Connection Between Approximation, Depth Separation and Learnability in Neural Networks"

6 / 6 papers shown

Title
Non-identifiability distinguishes Neural Networks among Parametric Models Sourav Chatterjee Timothy Sudijono 28 0 0 25 Apr 2025
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks Eshaan Nichani Alexandru Damian Jason D. Lee MLT 36 13 0 11 May 2023
Learning Single-Index Models with Shallow Neural Networks A. Bietti Joan Bruna Clayton Sanford M. Song 164 67 0 27 Oct 2022
Provable Regret Bounds for Deep Online Learning and Control Xinyi Chen Edgar Minasyan Jason D. Lee Elad Hazan 34 6 0 15 Oct 2021
Size and Depth Separation in Approximating Benign Functions with Neural Networks Gal Vardi Daniel Reichman T. Pitassi Ohad Shamir 21 7 0 30 Jan 2021
Benefits of depth in neural networks Matus Telgarsky 136 602 0 14 Feb 2016