A curated list of papers of interesting empirical study and insight on deep learning. Continually updating...

{KFAC,EKFAC,Diagonal,Implicit} Fisher Matrices and finite width NTKs in PyTorch

A fast, effective data attribution method for neural networks in PyTorch

Multi-framework implementation of Deep Kernel Shaping and Tailored Activation Transformations, which are methods that modify neural network models (and their initializations) to make them easier to train.

codebase for "A Theory of the Inductive Bias and Generalization of Kernel Regression and Wide Neural Networks"

We propose a lossless compression algorithm based on the NTK matrix for DNN. The compressed network yields asymptotically the same NTK as the original (dense and unquantized) network, with its weights and activations taking values only in {0, 1, -1} up to scaling.

Neural Tangent Kernel (NTK) module for the scikit-learn library

Official repository of our work "Finding Lottery Tickets in Vision Models via Data-driven Spectral Foresight Pruning" accepted at CVPR 2024

Study of the paper 'Neural Thompson Sampling' published in October 2020

Official Code: Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes

TF2 Implementation of Physics Informed Neural Networks and Neural Tangent Kernel

Code accompanying the paper "On the adaptation of recurrent neural networks for system identification"

A Python implementation of the Neural Tangent Kernel (jacot et al, 2018)

Implementation of "Deep Learning in Random Neural Fields: Numerical Experiments via Neural Tangent Kernel"

Code for "Learnware Reduced Kernel Mean Embedding Specification Based on Neural Tangent Kernel"

TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels

Empirical analysis of the Laplace and neural tangent kernel reproducing kernel Hilbert space (RKHS)

Understand the spectral bias of deep learning through the study of NTK

Senior Project for Statistics & Data Science at Yale University

Yale S&DS 432 final project studying lazy training dynamics for differentiable optimization problems