Scaling Neural Tangent Kernels via Sketching and Random Features

15 June 2021

Papers citing "Scaling Neural Tangent Kernels via Sketching and Random Features"

7 / 7 papers shown

Title
Tensor Sketch: Fast and Scalable Polynomial Kernel Approximation Ninh Pham Rasmus Pagh 99 0 0 13 May 2025
Optimal Kernel Quantile Learning with Random Features Caixing Wang Xingdong Feng 64 1 0 24 Aug 2024
A Framework and Benchmark for Deep Batch Active Learning for Regression David Holzmüller Viktor Zaverkin Johannes Kastner Ingo Steinwart UQCV BDL GP 61 34 0 17 Mar 2022
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks Sanjeev Arora S. Du Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang Dingli Yu AAML 47 162 0 03 Oct 2019
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks Yuan Cao Quanquan Gu MLT AI4CE 64 384 0 30 May 2019
Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation Greg Yang 69 286 0 13 Feb 2019
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks Difan Zou Yuan Cao Dongruo Zhou Quanquan Gu ODL 114 448 0 21 Nov 2018