konstantinos-p / Bayesian-Neural-Networks-Reading-ListLinks
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"
☆53Updated last year
Alternatives and similar repositories for Bayesian-Neural-Networks-Reading-List
Users that are interested in Bayesian-Neural-Networks-Reading-List are comparing it to the libraries listed below
Sorting:
- Simple (and cheap!) neural network uncertainty estimation☆66Updated last month
- Large-scale uncertainty benchmark in deep learning.☆60Updated 2 months ago
- Laplace approximations for Deep Learning.☆514Updated 2 months ago
- IVON optimizer for neural networks based on variational learning.☆68Updated 8 months ago
- PyTorch linear operators for curvature matrices (Hessian, Fisher/GGN, KFAC, ...)☆40Updated 2 months ago
- Agustinus' very opiniated publication-ready plotting library☆67Updated 2 months ago
- Bayesian active learning with EPIG data acquisition☆32Updated 2 months ago
- Mutual information estimators and benchmark☆51Updated 5 months ago
- This repository contains a Jax implementation of conformal training corresponding to the ICLR'22 paper "learning optimal conformal classi…☆130Updated 2 years ago
- Open-source framework for uncertainty and deep learning models in PyTorch☆409Updated 3 weeks ago
- All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)☆20Updated 3 years ago
- Sampling with gradient-based Markov Chain Monte Carlo approaches☆103Updated last year
- Code for the paper "Bayesian Neural Network Priors Revisited"☆57Updated 4 years ago
- ☆240Updated 2 years ago
- Official Implementation of "Transformers Can Do Bayesian Inference", the PFN paper☆221Updated 8 months ago
- ☆152Updated 2 years ago
- Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control☆67Updated 7 months ago
- Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, ca…☆173Updated last year
- Materials of the Nordic Probabilistic AI School 2022.☆181Updated 2 years ago
- Laplace Redux -- Effortless Bayesian Deep Learning☆42Updated last month
- Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".☆112Updated 3 years ago
- Public repo for course material on Bayesian machine learning at ENS Paris-Saclay and Univ Lille☆88Updated 4 months ago
- PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks☆451Updated 10 months ago
- Materials of the Nordic Probabilistic AI School 2023.☆90Updated last year
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 6 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆89Updated 4 years ago
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆24Updated 2 years ago
- Bayesianize: A Bayesian neural network wrapper in pytorch☆88Updated last year
- Optimal Transport Dataset Distance☆167Updated 3 years ago
- Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty☆142Updated 2 years ago