The Benefits of Reusing Batches for Gradient Descent in Two-Layer
Networks: Breaking the Curse of Information and Leap Exponents

The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents

5 February 2024

Emanuele Troiani

Lenka Zdeborová

Florent Krzakala

Papers citing "The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents"

5 / 5 papers shown

Title
Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input Ziang Chen Rong Ge MLT 53 1 0 10 Jan 2025
Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence Berfin Simsek Amire Bendjeddou Daniel Hsu 32 0 0 13 Nov 2024
Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions Luca Arnaboldi Yatin Dandi Florent Krzakala Luca Pesce Ludovic Stephan 59 11 0 24 May 2024
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks Blake Bordelon Abdulkadir Canatar C. Pehlevan 131 199 0 07 Feb 2020
Trainability and Accuracy of Neural Networks: An Interacting Particle System Approach Grant M. Rotskoff Eric Vanden-Eijnden 56 114 0 02 May 2018