Taylorized Training: Towards Better Approximation of Neural Network
Training at Finite Width

Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width

10 February 2020

Huan Wang

Papers citing "Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width"

9 / 9 papers shown

Title
Tight conditions for when the NTK approximation is valid Enric Boix-Adserà Etai Littwin 30 0 0 22 May 2023
Efficient Parametric Approximations of Neural Network Function Space Distance Nikita Dhawan Sicong Huang Juhan Bae Roger C. Grosse 16 5 0 07 Feb 2023
Catapult Dynamics and Phase Transitions in Quadratic Nets David Meltzer Junyu Liu 27 9 0 18 Jan 2023
The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning Massimiliano Incudini Michele Grossi Antonio Mandarino S. Vallecorsa Alessandra Di Pierro David Windridge 33 6 0 22 Dec 2022
Neural Networks can Learn Representations with Gradient Descent Alexandru Damian Jason D. Lee Mahdi Soltanolkotabi SSL MLT 22 114 0 30 Jun 2022
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials Eshaan Nichani Yunzhi Bai Jason D. Lee 29 10 0 08 Jun 2022
Neural Networks as Kernel Learners: The Silent Alignment Effect Alexander B. Atanasov Blake Bordelon Cengiz Pehlevan MLT 26 75 0 29 Oct 2021
LQF: Linear Quadratic Fine-Tuning Alessandro Achille Aditya Golatkar Avinash Ravichandran M. Polito Stefano Soatto 29 27 0 21 Dec 2020
Deep Networks and the Multiple Manifold Problem Sam Buchanan D. Gilboa John N. Wright 166 39 0 25 Aug 2020