Code to reproduce the experiments from our NeurIPS 2021 paper " The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective"

Last update: Dec 12, 2022

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Overview

Code

To run:

python runner.py new --save <SAVE_NAME> --data <PATH_TO_DATA_DIR> --dataset <DATASET> --model <model_name> [options] --n 1000 - train - test - kernel_fit - done

Options for Bayesian regression models are:

gp
gp_3l
gp_acos
gp_acos3
deep_gp_svi (options: --width 2)
deep_gp_hmc (options: --width 2)
deep_gp_3l2l_hmc (options: --width 2)
deep_gp_3l3l_hmc (options: --width1 2 --width2 2)
nn_hmc (options: --width 2)
nn_3l_hmc (options: --width 2)

Options for non-Bayesian classification models are:

nn (options: --widths 16,32,32)
lenet (options: --width 256 --conv-width 16)
resnet (options: --depth 14 --width 64)

Owner

Geoff Pleiss

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Code to reproduce the experiments from our NeurIPS 2021 paper " The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective"

Related tags

Overview

Code

Owner

Geoff Pleiss

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