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"
☆56Updated 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☆69Updated 3 months ago
- Large-scale uncertainty benchmark in deep learning.☆63Updated 4 months ago
- Laplace approximations for Deep Learning.☆517Updated 4 months ago
- Bayesian active learning with EPIG data acquisition☆35Updated 2 weeks ago
- PyTorch linear operators for curvature matrices (Hessian, Fisher/GGN, KFAC, ...)☆42Updated this week
- Mutual information estimators and benchmark☆52Updated 8 months ago
- IVON optimizer for neural networks based on variational learning.☆72Updated 10 months ago
- Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control☆69Updated 10 months ago
- All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)☆20Updated 3 years ago
- Open-source framework for uncertainty and deep learning models in PyTorch☆428Updated last week
- Agustinus' very opiniated publication-ready plotting library☆69Updated 4 months ago
- Laplace Redux -- Effortless Bayesian Deep Learning☆42Updated 3 months ago
- Sampling with gradient-based Markov Chain Monte Carlo approaches☆108Updated last year
- Materials of the Nordic Probabilistic AI School 2023.☆90Updated last year
- This repository contains a Jax implementation of conformal training corresponding to the ICLR'22 paper "learning optimal conformal classi…☆130Updated 3 years ago
- Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".☆36Updated 3 years ago
- Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, ca…☆176Updated last year
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 2 years ago
- Repo for the Tutorials of Day1-Day2 of the Nordic Probabilistic AI School 2023☆17Updated 2 years ago
- PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks☆457Updated last year
- Code for the paper "Bayesian Neural Network Priors Revisited"☆58Updated 4 years ago
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆24Updated 2 years ago
- Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".☆113Updated 3 years ago
- Materials of the Nordic Probabilistic AI School 2022.☆179Updated 3 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 6 years ago
- Example code of Sparse Gaussian Process Attention (ICLR 2023)☆25Updated this week
- Bayesianize: A Bayesian neural network wrapper in pytorch☆89Updated last year
- Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty☆144Updated 2 years ago
- ☆243Updated 2 years ago
- Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)☆78Updated 3 years ago