Limitations of the NTK for Understanding Generalization in Deep Learning

20 June 2022

Papers citing "Limitations of the NTK for Understanding Generalization in Deep Learning"

11 / 11 papers shown

Title
The Optimization Landscape of SGD Across the Feature Learning Strength Alexander B. Atanasov Alexandru Meterez James B. Simon C. Pehlevan 43 2 0 06 Oct 2024
How Feature Learning Can Improve Neural Scaling Laws Blake Bordelon Alexander B. Atanasov C. Pehlevan 54 12 0 26 Sep 2024
When does compositional structure yield compositional generalization? A kernel theory Samuel Lippl Kim Stachenfeld NAI CoGe 73 5 0 26 May 2024
Connecting NTK and NNGP: A Unified Theoretical Framework for Wide Neural Network Learning Dynamics Yehonatan Avidan Qianyi Li H. Sompolinsky 60 8 0 08 Sep 2023
Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models Guillermo Ortiz-Jiménez Alessandro Favero P. Frossard MoMe 42 106 0 22 May 2023
On the Stepwise Nature of Self-Supervised Learning James B. Simon Maksis Knutins Liu Ziyin Daniel Geisz Abraham J. Fetterman Joshua Albrecht SSL 32 29 0 27 Mar 2023
Behind the Scenes of Gradient Descent: A Trajectory Analysis via Basis Function Decomposition Jianhao Ma Li-Zhen Guo S. Fattahi 38 4 0 01 Oct 2022
On Kernel Regression with Data-Dependent Kernels James B. Simon BDL 17 3 0 04 Sep 2022
Geometric compression of invariant manifolds in neural nets J. Paccolat Leonardo Petrini Mario Geiger Kevin Tyloo M. Wyart MLT 52 34 0 22 Jul 2020
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks Blake Bordelon Abdulkadir Canatar C. Pehlevan 139 201 0 07 Feb 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 231 4,460 0 23 Jan 2020