When Do Neural Networks Outperform Kernel Methods?

24 June 2020

Papers citing "When Do Neural Networks Outperform Kernel Methods?"

47 / 47 papers shown

Title
Learning a Single Index Model from Anisotropic Data with vanilla Stochastic Gradient Descent Guillaume Braun Minh Ha Quang Masaaki Imaizumi MLT 42 0 0 31 Mar 2025
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
How Feature Learning Can Improve Neural Scaling Laws Blake Bordelon Alexander B. Atanasov Cengiz Pehlevan 57 12 0 26 Sep 2024
Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics Alireza Mousavi-Hosseini Denny Wu Murat A. Erdogdu MLT AI4CE 35 6 0 14 Aug 2024
Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions Luca Arnaboldi Yatin Dandi Florent Krzakala Luca Pesce Ludovic Stephan 70 12 0 24 May 2024
Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks Fanghui Liu L. Dadi V. Cevher 82 2 0 29 Apr 2024
$How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance$ How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance Hongkang Li Shuai Zhang Yihua Zhang Meng Wang Sijia Liu Pin-Yu Chen 41 4 0 12 Mar 2024
Function-Space Optimality of Neural Architectures with Multivariate Nonlinearities Rahul Parhi Michael Unser 44 5 0 05 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
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
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
Wide neural networks: From non-gaussian random fields at initialization to the NTK geometry of training Luís Carvalho Joao L. Costa José Mourao Gonccalo Oliveira AI4CE 26 1 0 06 Apr 2023
Learning time-scales in two-layers neural networks Raphael Berthier Andrea Montanari Kangjie Zhou 38 33 0 28 Feb 2023
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning François Caron Fadhel Ayed Paul Jung Hoileong Lee Juho Lee Hongseok Yang 62 2 0 02 Feb 2023
Bayes-optimal Learning of Deep Random Networks of Extensive-width Hugo Cui Florent Krzakala Lenka Zdeborová BDL 20 35 0 01 Feb 2023
Strong inductive biases provably prevent harmless interpolation Michael Aerni Marco Milanta Konstantin Donhauser Fanny Yang 35 9 0 18 Jan 2023
A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks Zhengdao Chen Eric Vanden-Eijnden Joan Bruna MLT 27 5 0 28 Oct 2022
Annihilation of Spurious Minima in Two-Layer ReLU Networks Yossi Arjevani M. Field 16 8 0 12 Oct 2022
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
Neural Networks can Learn Representations with Gradient Descent Alexandru Damian Jason D. Lee Mahdi Soltanolkotabi SSL MLT 22 114 0 30 Jun 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
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
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 40 121 0 03 May 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
Algorithmic Regularization in Model-free Overparametrized Asymmetric Matrix Factorization Liwei Jiang Yudong Chen Lijun Ding 40 26 0 06 Mar 2022
Kernel Two-Sample Tests in High Dimension: Interplay Between Moment Discrepancy and Dimension-and-Sample Orders J. Yan Xianyang Zhang 38 17 0 31 Dec 2021
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks P. Esser L. C. Vankadara D. Ghoshdastidar 28 53 0 07 Dec 2021
Learning with convolution and pooling operations in kernel methods Theodor Misiakiewicz Song Mei MLT 15 29 0 16 Nov 2021
Classification and Adversarial examples in an Overparameterized Linear Model: A Signal Processing Perspective Adhyyan Narang Vidya Muthukumar A. Sahai SILM AAML 36 1 0 27 Sep 2021
On the Power of Differentiable Learning versus PAC and SQ Learning Emmanuel Abbe Pritish Kamath Eran Malach Colin Sandon Nathan Srebro MLT 77 23 0 09 Aug 2021
Analytic Study of Families of Spurious Minima in Two-Layer ReLU Neural Networks: A Tale of Symmetry II Yossi Arjevani M. Field 28 18 0 21 Jul 2021
Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction Dominik Stöger Mahdi Soltanolkotabi ODL 42 75 0 28 Jun 2021
What can linearized neural networks actually say about generalization? Guillermo Ortiz-Jiménez Seyed-Mohsen Moosavi-Dezfooli P. Frossard 29 43 0 12 Jun 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
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs Gadi Naveh Zohar 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
On Energy-Based Models with Overparametrized Shallow Neural Networks Carles Domingo-Enrich A. Bietti Eric Vanden-Eijnden Joan Bruna BDL 33 9 0 15 Apr 2021
Learning with invariances in random features and kernel models Song Mei Theodor Misiakiewicz Andrea Montanari OOD 46 89 0 25 Feb 2021
Learning curves of generic features maps for realistic datasets with a teacher-student model Bruno Loureiro Cédric Gerbelot Hugo Cui Sebastian Goldt Florent Krzakala M. Mézard Lenka Zdeborová 35 135 0 16 Feb 2021
Adaptive and Oblivious Randomized Subspace Methods for High-Dimensional Optimization: Sharp Analysis and Lower Bounds Jonathan Lacotte Mert Pilanci 20 11 0 13 Dec 2020
Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime Andrea Agazzi Jianfeng Lu 13 15 0 22 Oct 2020
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 29 48 0 19 Oct 2020
Deep Networks and the Multiple Manifold Problem Sam Buchanan D. Gilboa John N. Wright 166 39 0 25 Aug 2020
The Interpolation Phase Transition in Neural Networks: Memorization and Generalization under Lazy Training Andrea Montanari Yiqiao Zhong 47 95 0 25 Jul 2020
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
Trainability and Accuracy of Neural Networks: An Interacting Particle System Approach Grant M. Rotskoff Eric Vanden-Eijnden 59 118 0 02 May 2018