A Theory of Neural Tangent Kernel Alignment and Its Influence on Training

29 May 2021

Papers citing "A Theory of Neural Tangent Kernel Alignment and Its Influence on Training"

10 / 10 papers shown

Title
Generalization through variance: how noise shapes inductive biases in diffusion models John J. Vastola DiffM 364 3 0 16 Apr 2025
Connecting NTK and NNGP: A Unified Theoretical Framework for Wide Neural Network Learning Dynamics Yehonatan Avidan Qianyi Li H. Sompolinsky 77 8 0 08 Sep 2023
A Framework and Benchmark for Deep Batch Active Learning for Regression David Holzmüller Viktor Zaverkin Johannes Kastner Ingo Steinwart UQCV BDL GP 59 34 0 17 Mar 2022
Neural Networks as Kernel Learners: The Silent Alignment Effect Alexander B. Atanasov Blake Bordelon Cengiz Pehlevan MLT 51 79 0 29 Oct 2021
The Principles of Deep Learning Theory Daniel A. Roberts Sho Yaida Boris Hanin FaML PINN GNN 21 245 0 18 Jun 2021
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel Stanislav Fort Gintare Karolina Dziugaite Mansheej Paul Sepideh Kharaghani Daniel M. Roy Surya Ganguli 82 187 0 28 Oct 2020
When Do Neural Networks Outperform Kernel Methods? Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari 74 188 0 24 Jun 2020
On the asymptotics of wide networks with polynomial activations Kyle Aitken Guy Gur-Ari 14 23 0 11 Jun 2020
A Fine-Grained Spectral Perspective on Neural Networks Greg Yang Hadi Salman 55 112 0 24 Jul 2019
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks Andrew M. Saxe James L. McClelland Surya Ganguli ODL 73 1,830 0 20 Dec 2013