Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS

22 September 2020

Papers citing "Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS"

50 / 69 papers shown

Title
Feature maps for the Laplacian kernel and its generalizations Sudhendu Ahir Parthe Pandit 55 0 0 24 Feb 2025
A Gap Between the Gaussian RKHS and Neural Networks: An Infinite-Center Asymptotic Analysis Akash Kumar Rahul Parhi Mikhail Belkin 41 0 0 22 Feb 2025
Which Spaces can be Embedded in $L_p$ -type Reproducing Kernel Banach Space? A Characterization via Metric Entropy Yiping Lu Daozhe Lin Qiang Du 34 0 0 14 Oct 2024
Variational Search Distributions Daniel M. Steinberg Rafael Oliveira Cheng Soon Ong Edwin V. Bonilla 33 0 0 10 Sep 2024
Approximation and Gradient Descent Training with Neural Networks G. Welper 36 1 0 19 May 2024
Nonparametric Teaching of Implicit Neural Representations Chen Zhang Steven Tin Sui Luo Jason Chun Lok Li Yik-Chung Wu Ngai Wong 38 2 0 17 May 2024
Multi-layer random features and the approximation power of neural networks Rustem Takhanov 27 1 0 26 Apr 2024
Laplace-HDC: Understanding the geometry of binary hyperdimensional computing Saeid Pourmand Wyatt D. Whiting Alireza Aghasi Nicholas F. Marshall 35 1 0 16 Apr 2024
Generalization in Kernel Regression Under Realistic Assumptions Daniel Barzilai Ohad Shamir 29 14 0 26 Dec 2023
How many Neurons do we need? A refined Analysis for Shallow Networks trained with Gradient Descent Mike Nguyen Nicole Mücke MLT 16 5 0 14 Sep 2023
Approximation Results for Gradient Descent trained Neural Networks G. Welper 48 0 0 09 Sep 2023
Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space Zhengdao Chen 30 1 0 03 Jul 2023
Benign Overfitting in Deep Neural Networks under Lazy Training Zhenyu Zhu Fanghui Liu Grigorios G. Chrysos Francesco Locatello V. Cevher AI4CE 18 10 0 30 May 2023
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension Moritz Haas David Holzmüller U. V. Luxburg Ingo Steinwart MLT 35 13 0 23 May 2023
Deep Learning with Kernels through RKHM and the Perron-Frobenius Operator Yuka Hashimoto Masahiro Ikeda Hachem Kadri 27 8 0 23 May 2023
ReLU soothes the NTK condition number and accelerates optimization for wide neural networks Chaoyue Liu Like Hui MLT 17 9 0 15 May 2023
Random Smoothing Regularization in Kernel Gradient Descent Learning Liang Ding Tianyang Hu Jiahan Jiang Donghao Li Wenjia Wang Yuan Yao 18 6 0 05 May 2023
On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains Yicheng Li Zixiong Yu Y. Cotronis Qian Lin 55 13 0 04 May 2023
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits Yusha Liu Aarti Singh 13 1 0 26 Apr 2023
Contrastive Learning Is Spectral Clustering On Similarity Graph Zhi-Hao Tan Yifan Zhang Jingqin Yang Yang Yuan SSL 54 18 0 27 Mar 2023
On Statistical Properties of Sharpness-Aware Minimization: Provable Guarantees Kayhan Behdin Rahul Mazumder 27 6 0 23 Feb 2023
A Kernel Perspective of Skip Connections in Convolutional Networks Daniel Barzilai Amnon Geifman Meirav Galun Ronen Basri 17 11 0 27 Nov 2022
An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks Jiayu Yao Yaniv Yacoby Beau Coker Weiwei Pan Finale Doshi-Velez 19 1 0 16 Nov 2022
Regularized Stein Variational Gradient Flow Ye He Krishnakumar Balasubramanian Bharath K. Sriperumbudur Jianfeng Lu OT 34 11 0 15 Nov 2022
Characterizing the Spectrum of the NTK via a Power Series Expansion Michael Murray Hui Jin Benjamin Bowman Guido Montúfar 30 11 0 15 Nov 2022
Few-shot Backdoor Attacks via Neural Tangent Kernels J. Hayase Sewoong Oh 30 21 0 12 Oct 2022
Approximation results for Gradient Descent trained Shallow Neural Networks in $1d$ R. Gentile G. Welper ODL 46 6 0 17 Sep 2022
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization) Zhenyu Zhu Fanghui Liu Grigorios G. Chrysos V. Cevher 39 19 0 15 Sep 2022
On the Trade-Off between Actionable Explanations and the Right to be Forgotten Martin Pawelczyk Tobias Leemann Asia J. Biega Gjergji Kasneci FaML MU 24 23 0 30 Aug 2022
Neural Tangent Kernel: A Survey Eugene Golikov Eduard Pokonechnyy Vladimir Korviakov 15 12 0 29 Aug 2022
Kernel Memory Networks: A Unifying Framework for Memory Modeling Georgios Iatropoulos Johanni Brea W. Gerstner 18 9 0 19 Aug 2022
A Sublinear Adversarial Training Algorithm Yeqi Gao Lianke Qin Zhao-quan Song Yitan Wang GAN 24 25 0 10 Aug 2022
Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting Neil Rohit Mallinar James B. Simon Amirhesam Abedsoltan Parthe Pandit M. Belkin Preetum Nakkiran 24 37 0 14 Jul 2022
Bounding the Width of Neural Networks via Coupled Initialization -- A Worst Case Analysis Alexander Munteanu Simon Omlor Zhao-quan Song David P. Woodruff 27 15 0 26 Jun 2022
Why Quantization Improves Generalization: NTK of Binary Weight Neural Networks Kaiqi Zhang Ming Yin Yu-Xiang Wang MQ 16 4 0 13 Jun 2022
Bandwidth Selection for Gaussian Kernel Ridge Regression via Jacobian Control Oskar Allerbo Rebecka Jörnsten 16 2 0 24 May 2022
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent Yiping Lu Jose H. Blanchet Lexing Ying 32 7 0 15 May 2022
On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels Amnon Geifman Meirav Galun David Jacobs Ronen Basri 21 13 0 17 Mar 2022
Cascaded Gaps: Towards Gap-Dependent Regret for Risk-Sensitive Reinforcement Learning Yingjie Fei Ruitu Xu 27 5 0 07 Mar 2022
Deep Learning in Random Neural Fields: Numerical Experiments via Neural Tangent Kernel Kaito Watanabe Kotaro Sakamoto Ryo Karakida Sho Sonoda S. Amari OOD 14 1 0 10 Feb 2022
Learning Representation from Neural Fisher Kernel with Low-rank Approximation Ruixiang Zhang Shuangfei Zhai Etai Littwin J. Susskind SSL 28 3 0 04 Feb 2022
A Generalized Weighted Optimization Method for Computational Learning and Inversion Bjorn Engquist Kui Ren Yunan Yang 23 4 0 23 Jan 2022
Complexity from Adaptive-Symmetries Breaking: Global Minima in the Statistical Mechanics of Deep Neural Networks Shaun Li AI4CE 30 0 0 03 Jan 2022
Understanding Square Loss in Training Overparametrized Neural Network Classifiers Tianyang Hu Jun Wang Wenjia Wang Zhenguo Li UQCV AAML 33 19 0 07 Dec 2021
Learning Curves for Continual Learning in Neural Networks: Self-Knowledge Transfer and Forgetting Ryo Karakida S. Akaho CLL 24 11 0 03 Dec 2021
Understanding Layer-wise Contributions in Deep Neural Networks through Spectral Analysis Yatin Dandi Arthur Jacot FAtt 21 4 0 06 Nov 2021
Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning Yingjie Fei Zhuoran Yang Yudong Chen Zhaoran Wang 33 46 0 06 Nov 2021
Uniform Generalization Bounds for Overparameterized Neural Networks Sattar Vakili Michael Bromberg Jezabel R. Garcia Da-shan Shiu A. Bernacchia 17 19 0 13 Sep 2021
A spectral-based analysis of the separation between two-layer neural networks and linear methods Lei Wu Jihao Long 16 8 0 10 Aug 2021
Deep Networks Provably Classify Data on Curves Tingran Wang Sam Buchanan D. Gilboa John N. Wright 23 9 0 29 Jul 2021