Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit

3 June 2024

Papers citing "Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit"

8 / 8 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
Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks Fanghui Liu L. Dadi V. Cevher 61 2 0 29 Apr 2024
Nonlinear spiked covariance matrices and signal propagation in deep neural networks Zhichao Wang Denny Wu Zhou Fan 27 7 0 15 Feb 2024
The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents Yatin Dandi Emanuele Troiani Luca Arnaboldi Luca Pesce Lenka Zdeborová Florent Krzakala MLT 56 24 0 05 Feb 2024
SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics Emmanuel Abbe Enric Boix-Adserà Theodor Misiakiewicz FedML MLT 76 72 0 21 Feb 2023
Learning Single-Index Models with Shallow Neural Networks A. Bietti Joan Bruna Clayton Sanford M. Song 150 65 0 27 Oct 2022
Neural Networks Efficiently Learn Low-Dimensional Representations with SGD Alireza Mousavi-Hosseini Sejun Park M. Girotti Ioannis Mitliagkas Murat A. Erdogdu MLT 310 48 0 29 Sep 2022