A curated list of papers of interesting empirical study and insight on deep learning. Continually updating...
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Updated
Aug 26, 2024
A curated list of papers of interesting empirical study and insight on deep learning. Continually updating...
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.
Implementation of "Deep Learning in Random Neural Fields: Numerical Experiments via Neural Tangent Kernel"
A fast, effective data attribution method for neural networks in PyTorch
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
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.
Code for "Learnware Reduced Kernel Mean Embedding Specification Based on Neural Tangent Kernel"
codebase for "A Theory of the Inductive Bias and Generalization of Kernel Regression and Wide Neural Networks"
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
Senior Project for Statistics & Data Science at Yale University
TF2 Implementation of Physics Informed Neural Networks and Neural Tangent Kernel
Implementation of Approximate Smooth Kernel Value Iteration
Study of the paper 'Neural Thompson Sampling' published in October 2020
Coursework of MIT 6.S088
Empirical analysis of the Laplace and neural tangent kernel reproducing kernel Hilbert space (RKHS)
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
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