On the Inductive Bias of Neural Tangent Kernels

29 May 2019

Papers citing "On the Inductive Bias of Neural Tangent Kernels"

43 / 43 papers shown

Title
Generalization through variance: how noise shapes inductive biases in diffusion models John J. Vastola DiffM 416 4 0 16 Apr 2025
Inductive Gradient Adjustment For Spectral Bias In Implicit Neural Representations Kexuan Shi Hai Chen Leheng Zhang Shuhang Gu 72 1 0 17 Oct 2024
Geometric Inductive Biases of Deep Networks: The Role of Data and Architecture Sajad Movahedi Antonio Orvieto Seyed-Mohsen Moosavi-Dezfooli AI4CE AAML 478 0 0 15 Oct 2024
On the Shift Invariance of Max Pooling Feature Maps in Convolutional Neural Networks Hubert Leterme K. Polisano V. Perrier Alahari Karteek FAtt 93 2 0 19 Sep 2022
Implicit Kinematic Policies: Unifying Joint and Cartesian Action Spaces in End-to-End Robot Learning Aditya Ganapathi Peter R. Florence Jacob Varley Kaylee Burns Ken Goldberg Andy Zeng 166 16 0 03 Mar 2022
Benign Overfitting in Linear Regression Peter L. Bartlett Philip M. Long Gábor Lugosi Alexander Tsigler MLT 67 777 0 26 Jun 2019
The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies Ronen Basri David Jacobs Yoni Kasten S. Kritchman 64 218 0 02 Jun 2019
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks Yuan Cao Quanquan Gu MLT AI4CE 80 389 0 30 May 2019
Linearized two-layers neural networks in high dimension Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari MLT 45 243 0 27 Apr 2019
On Exact Computation with an Infinitely Wide Neural Net Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang 209 922 0 26 Apr 2019
On the Power and Limitations of Random Features for Understanding Neural Networks Gilad Yehudai Ohad Shamir MLT 66 182 0 01 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 196 1,099 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 68 278 0 16 Feb 2019
How do infinite width bounded norm networks look in function space? Pedro H. P. Savarese Itay Evron Daniel Soudry Nathan Srebro 72 165 0 13 Feb 2019
Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation Greg Yang 137 287 0 13 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 185 971 0 24 Jan 2019
On Lazy Training in Differentiable Programming Lénaïc Chizat Edouard Oyallon Francis R. Bach 102 833 0 19 Dec 2018
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks Difan Zou Yuan Cao Dongruo Zhou Quanquan Gu ODL 174 448 0 21 Nov 2018
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers Zeyuan Allen-Zhu Yuanzhi Li Yingyu Liang MLT 179 769 0 12 Nov 2018
A Convergence Theory for Deep Learning via Over-Parameterization Zeyuan Allen-Zhu Yuanzhi Li Zhao Song AI4CE ODL 238 1,462 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 188 1,134 0 09 Nov 2018
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes Roman Novak Lechao Xiao Jaehoon Lee Yasaman Bahri Greg Yang Jiri Hron Daniel A. Abolafia Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 54 309 0 11 Oct 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 210 1,270 0 04 Oct 2018
Deep Convolutional Networks as shallow Gaussian Processes Adrià Garriga-Alonso C. Rasmussen Laurence Aitchison BDL UQCV 102 270 0 16 Aug 2018
Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data Yuanzhi Li Yingyu Liang MLT 212 653 0 03 Aug 2018
Just Interpolate: Kernel "Ridgeless" Regression Can Generalize Tengyuan Liang Alexander Rakhlin 60 353 0 01 Aug 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 252 3,194 0 20 Jun 2018
On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport Lénaïc Chizat Francis R. Bach OT 200 735 0 24 May 2018
Gaussian Process Behaviour in Wide Deep Neural Networks A. G. Matthews Mark Rowland Jiri Hron Richard Turner Zoubin Ghahramani BDL 141 559 0 30 Apr 2018
A Mean Field View of the Landscape of Two-Layers Neural Networks Song Mei Andrea Montanari Phan-Minh Nguyen MLT 81 858 0 18 Apr 2018
To understand deep learning we need to understand kernel learning M. Belkin Siyuan Ma Soumik Mandal 57 418 0 05 Feb 2018
Deep Neural Networks as Gaussian Processes Jaehoon Lee Yasaman Bahri Roman Novak S. Schoenholz Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 115 1,093 0 01 Nov 2017
The Implicit Bias of Gradient Descent on Separable Data Daniel Soudry Elad Hoffer Mor Shpigel Nacson Suriya Gunasekar Nathan Srebro 147 916 0 27 Oct 2017
Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm Simon Fischer Ingo Steinwart 166 151 0 23 Feb 2017
Diverse Neural Network Learns True Target Functions Bo Xie Yingyu Liang Le Song 163 137 0 09 Nov 2016
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks Julien Mairal SSL 62 130 0 20 May 2016
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity Amit Daniely Roy Frostig Y. Singer 156 343 0 18 Feb 2016
Generalization Properties of Learning with Random Features Alessandro Rudi Lorenzo Rosasco MLT 68 331 0 14 Feb 2016
Breaking the Curse of Dimensionality with Convex Neural Networks Francis R. Bach 176 706 0 30 Dec 2014
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning Behnam Neyshabur Ryota Tomioka Nathan Srebro AI4CE 88 657 0 20 Dec 2014
Convolutional Kernel Networks Julien Mairal Piotr Koniusz Zaïd Harchaoui Cordelia Schmid 87 380 0 12 Jun 2014
Invariant Scattering Convolution Networks Joan Bruna S. Mallat 115 1,275 0 05 Mar 2012
Group Invariant Scattering S. Mallat 116 988 0 12 Jan 2011