bayesian-neural-networks-reading-list

A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesian Deep Learning, by providing an overview of key papers. More details: "A Primer on Bayesian Neural Networks: Review and Debates"

cold_posteriors_pac_bayes

The code for the paper "Cold Posteriors through PAC-Bayes". https://arxiv.org/pdf/2206.11173.pdf

dnn_pruning_and_accuracy.

We conduct experiments on pruning DNN layers and it's effect on network accuracy. We also explore the relationship betweeen the intrinsic dimensionality of the data and the network robustness to pruning.

feta_fully_connected

Experiments comparing FeTa and Hard Thresholding for fully connected architectures.

image_classification_sota

This repository aims to understand and implement the state of the art in image classification (as of November 2021) in PyTorch.

kassiope_algorithms

konstantinos-p.github.io

My personal site.

pac_bayes_invariance

Code from the paper "PAC-Bayesian Margin Bounds for convolutional neural networks". https://arxiv.org/pdf/1905.09677.pdf

scikit-learn

scikit-learn: machine learning in Python

something_for_almost_nothing

Code for the paper "Something for (almost) nothing: improving deep ensemble calibration using unlabeled data"

wide_resnet_wfixup_jax

An implementation of WideResNets with Fixup initialization in Jax/Flax. This can be useful for use cases where Batch Normalization should be avoided (for example when using the Laplace approximation to the Bayesian posterior).

yarn-mistral-flax

An implementation of yarn-mistral-7B in flax. This implementation is based on the pytorch version uploaded to huggingface.