Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits

21 January 2019

Papers citing "Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits"

11 / 11 papers shown

Title
Quantitative CLTs in Deep Neural Networks Stefano Favaro Boris Hanin Domenico Marinucci I. Nourdin G. Peccati BDL 33 12 0 12 Jul 2023
Tight Convergence Rate Bounds for Optimization Under Power Law Spectral Conditions Maksim Velikanov Dmitry Yarotsky 9 6 0 02 Feb 2022
Learning Operators with Coupled Attention Georgios Kissas Jacob H. Seidman Leonardo Ferreira Guilhoto V. Preciado George J. Pappas P. Perdikaris 32 110 0 04 Jan 2022
Classification and Adversarial examples in an Overparameterized Linear Model: A Signal Processing Perspective Adhyyan Narang Vidya Muthukumar A. Sahai SILM AAML 36 1 0 27 Sep 2021
Geometric compression of invariant manifolds in neural nets J. Paccolat Leonardo Petrini Mario Geiger Kevin Tyloo M. Wyart MLT 55 34 0 22 Jul 2020
$A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-$\ell_1$-Norm Interpolated Classifiers$ A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum- $\ell_1$ -Norm Interpolated Classifiers Tengyuan Liang Pragya Sur 35 68 0 05 Feb 2020
Neural Spectrum Alignment: Empirical Study Dmitry Kopitkov Vadim Indelman 29 14 0 19 Oct 2019
The Local Elasticity of Neural Networks Hangfeng He Weijie J. Su 40 44 0 15 Oct 2019
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 60 626 0 14 Aug 2019
Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems Tianle Cai Ruiqi Gao Jikai Hou Siyu Chen Dong Wang Di He Zhihua Zhang Liwei Wang ODL 21 57 0 28 May 2019
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel Colin Wei J. Lee Qiang Liu Tengyu Ma 23 245 0 12 Oct 2018