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On the linearity of large non-linear models: when and why the tangent kernel is constant

2 October 2020

Papers citing "On the linearity of large non-linear models: when and why the tangent kernel is constant"

22 / 22 papers shown

Title
Supervised Models Can Generalize Also When Trained on Random Labels Oskar Allerbo Thomas B. Schön OOD SSL 94 0 0 16 May 2025
Peer-to-Peer Learning Dynamics of Wide Neural Networks Shreyas Chaudhari Srinivasa Pranav Emile Anand José M. F. Moura 84 3 0 23 Sep 2024
Variational Search Distributions Daniel M. Steinberg Rafael Oliveira Cheng Soon Ong Edwin V. Bonilla 89 1 0 10 Sep 2024
Continual learning with the neural tangent ensemble Ari S. Benjamin Christian Pehle Kyle Daruwalla UQCV 122 1 0 30 Aug 2024
Simplifying Deep Temporal Difference Learning Matteo Gallici Mattie Fellows Benjamin Ellis B. Pou Ivan Masmitja Jakob Foerster Mario Martin OffRL 120 26 0 05 Jul 2024
Loss Gradient Gaussian Width based Generalization and Optimization Guarantees A. Banerjee Qiaobo Li Yingxue Zhou 125 0 0 11 Jun 2024
Reparameterization invariance in approximate Bayesian inference Hrittik Roy M. Miani Carl Henrik Ek Philipp Hennig Marvin Pfortner Lukas Tatzel Søren Hauberg BDL 79 9 0 05 Jun 2024
Any Target Function Exists in a Neighborhood of Any Sufficiently Wide Random Network: A Geometrical Perspective S. Amari 51 12 0 20 Jan 2020
Optimization for deep learning: theory and algorithms Ruoyu Sun ODL 124 169 0 19 Dec 2019
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks Ziwei Ji Matus Telgarsky 66 178 0 26 Sep 2019
Linearized two-layers neural networks in high dimension Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari MLT 60 243 0 27 Apr 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 211 1,106 0 18 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 202 973 0 24 Jan 2019
On Lazy Training in Differentiable Programming Lénaïc Chizat Edouard Oyallon Francis R. Bach 111 839 0 19 Dec 2018
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks Difan Zou Yuan Cao Dongruo Zhou Quanquan Gu ODL 190 448 0 21 Nov 2018
A Convergence Theory for Deep Learning via Over-Parameterization Zeyuan Allen-Zhu Yuanzhi Li Zhao Song AI4CE ODL 264 1,466 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 209 1,135 0 09 Nov 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 227 1,275 0 04 Oct 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 269 3,213 0 20 Jun 2018
Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks Peter L. Bartlett D. Helmbold Philip M. Long 88 117 0 16 Feb 2018
Searching for Activation Functions Prajit Ramachandran Barret Zoph Quoc V. Le 69 609 0 16 Oct 2017
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,322 0 10 Dec 2015