Bayesian Deep Learning


A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG).

Overview

Deep Learning Is Not Good Enough, We Need Bayesian Deep Learning
In this post I’m going to introduce Bayesian deep learning (BDL), which provides a deep learning framework which can also model uncertainty.
bayesian-deep-learning uncertainty deep-learning tutorial
The Case for Bayesian Deep Learning
What makes Bayesian inference distinctive, and why Bayesian inference is worthwhile in deep learning.
bayesian-deep-learning bayesian-neural-networks bayesian-inference tutorial

Tutorials

Bayesian Neural Networks Need Not Concentrate
We show that if the prior does not distinguish between functions that generalize and functions that don’t, Bayesian inference cannot provide uncertainties.
bayesian-deep-learning bayesian-neural-networks tutorial article

Libraries

General
BLiTZ — A Bayesian Neural Network library for PyTorch
Bayesian Layers in Torch Zoo is a simple and extensible library to create Bayesian Neural Network layers on the top of PyTorch.
bayesian-deep-learning pytorch blitz bayesian-neural-networks
Bayesian Active Learning (BaaL)
BaaL is an active learning library by ElementAI. This repository contains techniques and reusable components to make active learning accessible for all.
active-learning bayesian-deep-learning baal elementai
DoWhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions.
causal-inference bayesian-deep-learning dowhy microsoft
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