konstantinos-p/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"
This is a curated reading list for researchers delving into Bayesian Neural Networks (BNNs), specifically focusing on models where uncertainty is defined over network weights for classification tasks. It provides an overview of essential and recent academic papers, helping new researchers understand key concepts and approximate inference methods like Variational Inference and Laplace approximation. The target audience is academic researchers, PhD students, or machine learning scientists exploring advanced deep learning techniques.
No commits in the last 6 months.
Use this if you are a researcher or student looking for a structured guide to understand the core concepts and recent advancements in Bayesian Neural Networks for classification problems.
Not ideal if you are a practitioner looking for immediately implementable code or a general overview of machine learning without a deep dive into advanced Bayesian methods.
Stars
59
Forks
5
Language
—
License
—
Category
Last pushed
Nov 01, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/konstantinos-p/Bayesian-Neural-Networks-Reading-List"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
tensorflow/probability
Probabilistic reasoning and statistical analysis in TensorFlow
pyro-ppl/pyro
Deep universal probabilistic programming with Python and PyTorch
erdogant/bnlearn
Python package for Causal Discovery by learning the graphical structure of Bayesian networks....
probml/pyprobml
Python code for "Probabilistic Machine learning" book by Kevin Murphy
google/edward2
A simple probabilistic programming language.