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

Last update: Dec 02, 2022

Overview

Eigenlearning

This repo contains code for replicating the experiments of the paper A Theory of the Inductive Bias and Generalization of Kernel Regression and Wide Neural Networks. The .py files provide a general codebase for generating synthetic datasets, loading image datasets, and testing the performance of kernel regression and finite nets learning functions on these domains. The .ipynb files in the experiments directory run selected experiments and generate the associated figures.

As of Spring 2022, this repo is under active development. If you discover any bugs or want to request any changes, let us know!

Owner

Jamie Simon

Berkeley physics PhD student aiming to build fundamental understanding of deep learning

GitHub Repository https://arxiv.org/abs/2110.03922

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

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