A fast, effective data attribution method for neural networks in PyTorch
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Updated
Jun 7, 2024 - Python
A fast, effective data attribution method for neural networks in PyTorch
codebase for "A Theory of the Inductive Bias and Generalization of Kernel Regression and Wide Neural Networks"
A curated list of papers of interesting empirical study and insight on deep learning. Continually updating...
Study of the paper 'Neural Thompson Sampling' published in October 2020
Official repository of our work "Finding Lottery Tickets in Vision Models via Data-driven Spectral Foresight Pruning" accepted at CVPR 2024
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
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.
Code accompanying the paper "On the adaptation of recurrent neural networks for system identification"
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.
A Python implementation of the Neural Tangent Kernel (jacot et al, 2018)
TF2 Implementation of Physics Informed Neural Networks and Neural Tangent Kernel
Implementation of "Deep Learning in Random Neural Fields: Numerical Experiments via Neural Tangent Kernel"
Code for paper
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
Official Code: Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes
Implementation of Approximate Smooth Kernel Value Iteration
Code for "Learnware Reduced Kernel Mean Embedding Specification Based on Neural Tangent Kernel"
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