Deep learning versus kernel learning: an empirical study of loss
landscape geometry and the time evolution of the Neural Tangent Kernel

Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel

28 October 2020

Gintare Karolina Dziugaite

Sepideh Kharaghani

Papers citing "Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel"

10 / 60 papers shown

Title
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective Geoff Pleiss John P. Cunningham 28 24 0 11 Jun 2021
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes James Lucas Juhan Bae Michael Ruogu Zhang Stanislav Fort R. Zemel Roger C. Grosse MoMe 172 28 0 22 Apr 2021
Intraclass clustering: an implicit learning ability that regularizes DNNs Simon Carbonnelle Christophe De Vleeschouwer 65 8 0 11 Mar 2021
Experiments with Rich Regime Training for Deep Learning Xinyan Li A. Banerjee 32 2 0 26 Feb 2021
Learning Neural Network Subspaces Mitchell Wortsman Maxwell Horton Carlos Guestrin Ali Farhadi Mohammad Rastegari UQCV 27 85 0 20 Feb 2021
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise Spencer Frei Yuan Cao Quanquan Gu FedML MLT 70 19 0 04 Jan 2021
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics D. Kunin Javier Sagastuy-Breña Surya Ganguli Daniel L. K. Yamins Hidenori Tanaka 107 77 0 08 Dec 2020
Spectral Bias and Task-Model Alignment Explain Generalization in Kernel Regression and Infinitely Wide Neural Networks Abdulkadir Canatar Blake Bordelon Cengiz Pehlevan 34 181 0 23 Jun 2020
The large learning rate phase of deep learning: the catapult mechanism Aitor Lewkowycz Yasaman Bahri Ethan Dyer Jascha Narain Sohl-Dickstein Guy Gur-Ari ODL 159 236 0 04 Mar 2020
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 312 2,896 0 15 Sep 2016