Towards Understanding Hierarchical Learning: Benefits of Neural
Representations

Towards Understanding Hierarchical Learning: Benefits of Neural Representations

24 June 2020

Huan Wang

Papers citing "Towards Understanding Hierarchical Learning: Benefits of Neural Representations"

12 / 12 papers shown

Title
Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks Matteo Tucat Anirbit Mukherjee Procheta Sen Mingfei Sun Omar Rivasplata MLT 39 1 0 12 Apr 2024
Tight conditions for when the NTK approximation is valid Enric Boix-Adserà Etai Littwin 35 0 0 22 May 2023
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks Eshaan Nichani Alexandru Damian Jason D. Lee MLT 44 13 0 11 May 2023
The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning Massimiliano Incudini Michele Grossi Antonio Mandarino S. Vallecorsa Alessandra Di Pierro David Windridge 33 6 0 22 Dec 2022
Learning Single-Index Models with Shallow Neural Networks A. Bietti Joan Bruna Clayton Sanford M. Song 170 68 0 27 Oct 2022
Neural Networks can Learn Representations with Gradient Descent Alexandru Damian Jason D. Lee Mahdi Soltanolkotabi SSL MLT 25 114 0 30 Jun 2022
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials Eshaan Nichani Yunzhi Bai Jason D. Lee 29 10 0 08 Jun 2022
On Feature Learning in Neural Networks with Global Convergence Guarantees Zhengdao Chen Eric Vanden-Eijnden Joan Bruna MLT 36 13 0 22 Apr 2022
Optimization-Based Separations for Neural Networks Itay Safran Jason D. Lee 185 14 0 04 Dec 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective Geoff Pleiss John P. Cunningham 28 24 0 11 Jun 2021
Deep Equals Shallow for ReLU Networks in Kernel Regimes A. Bietti Francis R. Bach 30 86 0 30 Sep 2020
Benefits of depth in neural networks Matus Telgarsky 153 603 0 14 Feb 2016