Classifying high-dimensional Gaussian mixtures: Where kernel methods
fail and neural networks succeed

Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed

23 February 2021

Maria Refinetti

Sebastian Goldt

Florent Krzakala

Lenka Zdeborová

Papers citing "Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed"

18 / 18 papers shown

Title
Asymptotic Analysis of Two-Layer Neural Networks after One Gradient Step under Gaussian Mixtures Data with Structure Samet Demir Zafer Dogan MLT 34 0 0 02 Mar 2025
Classifying Overlapping Gaussian Mixtures in High Dimensions: From Optimal Classifiers to Neural Nets Khen Cohen Noam Levi Yaron Oz BDL 31 1 0 28 May 2024
Spectral alignment of stochastic gradient descent for high-dimensional classification tasks Gerard Ben Arous Reza Gheissari Jiaoyang Huang Aukosh Jagannath 29 14 0 04 Oct 2023
Gradient-Based Feature Learning under Structured Data Alireza Mousavi-Hosseini Denny Wu Taiji Suzuki Murat A. Erdogdu MLT 37 18 0 07 Sep 2023
Quantifying lottery tickets under label noise: accuracy, calibration, and complexity V. Arora Daniele Irto Sebastian Goldt G. Sanguinetti 36 2 0 21 Jun 2023
The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions Nishil Patel Sebastian Lee Stefano Sarao Mannelli Sebastian Goldt Adrew Saxe OffRL 28 3 0 17 Jun 2023
From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks Luca Arnaboldi Ludovic Stephan Florent Krzakala Bruno Loureiro MLT 35 31 0 12 Feb 2023
Neural Networks Efficiently Learn Low-Dimensional Representations with SGD Alireza Mousavi-Hosseini Sejun Park M. Girotti Ioannis Mitliagkas Murat A. Erdogdu MLT 324 48 0 29 Sep 2022
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit Boaz Barak Benjamin L. Edelman Surbhi Goel Sham Kakade Eran Malach Cyril Zhang 33 123 0 18 Jul 2022
Learning sparse features can lead to overfitting in neural networks Leonardo Petrini Francesco Cagnetta Eric Vanden-Eijnden M. Wyart MLT 42 23 0 24 Jun 2022
High-dimensional limit theorems for SGD: Effective dynamics and critical scaling Gerard Ben Arous Reza Gheissari Aukosh Jagannath 59 58 0 08 Jun 2022
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation Jimmy Ba Murat A. Erdogdu Taiji Suzuki Zhichao Wang Denny Wu Greg Yang MLT 37 121 0 03 May 2022
Contrasting random and learned features in deep Bayesian linear regression Jacob A. Zavatone-Veth William L. Tong C. Pehlevan BDL MLT 28 26 0 01 Mar 2022
Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks R. Veiga Ludovic Stephan Bruno Loureiro Florent Krzakala Lenka Zdeborová MLT 10 31 0 01 Feb 2022
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs Gadi Naveh Z. Ringel SSL MLT 36 31 0 08 Jun 2021
Relative stability toward diffeomorphisms indicates performance in deep nets Leonardo Petrini Alessandro Favero Mario Geiger M. Wyart OOD 38 15 0 06 May 2021
Geometric compression of invariant manifolds in neural nets J. Paccolat Leonardo Petrini Mario Geiger Kevin Tyloo M. Wyart MLT 55 34 0 22 Jul 2020
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond Fanghui Liu Xiaolin Huang Yudong Chen Johan A. K. Suykens BDL 44 172 0 23 Apr 2020