Implementation of Approximate Smooth Kernel Value Iteration
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Updated
Oct 12, 2022 - Python
Implementation of Approximate Smooth Kernel Value Iteration
Code for paper
Yale S&DS 432 final project studying lazy training dynamics for differentiable optimization problems
Coursework of MIT 6.S088
Senior Project for Statistics & Data Science at Yale University
Understand the spectral bias of deep learning through the study of NTK
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
Empirical analysis of the Laplace and neural tangent kernel reproducing kernel Hilbert space (RKHS)
Implementation of "Deep Learning in Random Neural Fields: Numerical Experiments via Neural Tangent Kernel"
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A Python implementation of the Neural Tangent Kernel (jacot et al, 2018)
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"
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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
Neural Tangent Kernel (NTK) module for the scikit-learn library
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.
codebase for "A Theory of the Inductive Bias and Generalization of Kernel Regression and Wide Neural Networks"
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.
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