Linearized two-layers neural networks in high dimension

27 April 2019

Papers citing "Linearized two-layers neural networks in high dimension"

34 / 34 papers shown

Title
Fine-Grained Verifiers: Preference Modeling as Next-token Prediction in Vision-Language Alignment Chenhang Cui An Zhang Yiyang Zhou Zhaorun Chen Gelei Deng Huaxiu Yao Tat-Seng Chua 110 5 0 18 Oct 2024
Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis Yufan Li Subhabrata Sen Ben Adlam MLT 119 1 0 18 Apr 2024
Nonlinear Meta-Learning Can Guarantee Faster Rates Dimitri Meunier Zhu Li Arthur Gretton Samory Kpotufe 66 6 0 20 Jul 2023
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks Eshaan Nichani Alexandru Damian Jason D. Lee MLT 118 15 0 11 May 2023
Learning time-scales in two-layers neural networks Raphael Berthier Andrea Montanari Kangjie Zhou 89 33 0 28 Feb 2023
Generalisation under gradient descent via deterministic PAC-Bayes Eugenio Clerico Tyler Farghly George Deligiannidis Benjamin Guedj Arnaud Doucet 54 4 0 06 Sep 2022
On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels Tengyuan Liang Alexander Rakhlin Xiyu Zhai 44 29 0 27 Aug 2019
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 73 631 0 14 Aug 2019
Limitations of Lazy Training of Two-layers Neural Networks Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari MLT 45 143 0 21 Jun 2019
Disentangling feature and lazy training in deep neural networks Mario Geiger S. Spigler Arthur Jacot Matthieu Wyart 79 17 0 19 Jun 2019
On the Power and Limitations of Random Features for Understanding Neural Networks Gilad Yehudai Ohad Shamir MLT 53 182 0 01 Apr 2019
Two models of double descent for weak features M. Belkin Daniel J. Hsu Ji Xu 80 375 0 18 Mar 2019
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent Jaehoon Lee Lechao Xiao S. Schoenholz Yasaman Bahri Roman Novak Jascha Narain Sohl-Dickstein Jeffrey Pennington 153 1,089 0 18 Feb 2019
Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit Song Mei Theodor Misiakiewicz Andrea Montanari MLT 68 277 0 16 Feb 2019
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruosong Wang MLT 142 966 0 24 Jan 2019
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 174 1,628 0 28 Dec 2018
On Lazy Training in Differentiable Programming Lénaïc Chizat Edouard Oyallon Francis R. Bach 89 823 0 19 Dec 2018
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks Difan Zou Yuan Cao Dongruo Zhou Quanquan Gu ODL 133 448 0 21 Nov 2018
A Convergence Theory for Deep Learning via Over-Parameterization Zeyuan Allen-Zhu Yuanzhi Li Zhao Song AI4CE ODL 190 1,457 0 09 Nov 2018
Gradient Descent Finds Global Minima of Deep Neural Networks S. Du Jason D. Lee Haochuan Li Liwei Wang Masayoshi Tomizuka ODL 143 1,133 0 09 Nov 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 155 1,261 0 04 Oct 2018
Does data interpolation contradict statistical optimality? M. Belkin Alexander Rakhlin Alexandre B. Tsybakov 64 218 0 25 Jun 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 193 3,160 0 20 Jun 2018
On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport Lénaïc Chizat Francis R. Bach OT 157 731 0 24 May 2018
Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial Convergence and SQ Lower Bounds Santosh Vempala John Wilmes MLT 69 50 0 07 May 2018
A Mean Field View of the Landscape of Two-Layers Neural Networks Song Mei Andrea Montanari Phan-Minh Nguyen MLT 76 855 0 18 Apr 2018
To understand deep learning we need to understand kernel learning M. Belkin Siyuan Ma Soumik Mandal 44 418 0 05 Feb 2018
The Implicit Bias of Gradient Descent on Separable Data Daniel Soudry Elad Hoffer Mor Shpigel Nacson Suriya Gunasekar Nathan Srebro 83 908 0 27 Oct 2017
The Landscape of Empirical Risk for Non-convex Losses Song Mei Yu Bai Andrea Montanari 52 312 0 22 Jul 2016
Generalization Properties of Learning with Random Features Alessandro Rudi Lorenzo Rosasco MLT 60 330 0 14 Feb 2016
Breaking the Curse of Dimensionality with Convex Neural Networks Francis R. Bach 121 703 0 30 Dec 2014
Sharp analysis of low-rank kernel matrix approximations Francis R. Bach 116 282 0 09 Aug 2012
On information plus noise kernel random matrices N. Karoui 54 65 0 11 Nov 2010
The spectrum of kernel random matrices N. Karoui 131 223 0 04 Jan 2010