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.☆62Updated 3 months ago
- Laplace approximations for Deep Learning.☆515Updated 4 months ago
- Mutual information estimators and benchmark☆51Updated 7 months ago
- PyTorch linear operators for curvature matrices (Hessian, Fisher/GGN, KFAC, ...)☆41Updated this week
- IVON optimizer for neural networks based on variational learning.☆70Updated 9 months ago
- Bayesian active learning with EPIG data acquisition☆34Updated 4 months ago
- All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)☆20Updated 3 years ago
- Laplace Redux -- Effortless Bayesian Deep Learning☆42Updated 2 months ago
- Agustinus' very opiniated publication-ready plotting library☆69Updated 3 months ago
- Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control☆68Updated 9 months ago
- Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".☆112Updated 3 years ago
- This repository contains a Jax implementation of conformal training corresponding to the ICLR'22 paper "learning optimal conformal classi…☆129Updated 3 years ago
- Materials of the Nordic Probabilistic AI School 2023.☆90Updated last year
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆24Updated 2 years ago
- Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, ca…☆174Updated last year
- Code for the paper "Bayesian Neural Network Priors Revisited"☆57Updated 4 years ago
- A package for conformal prediction with conditional guarantees.☆62Updated 6 months ago
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 2 years ago
- ☆243Updated 2 years ago
- Official Implementation of "Transformers Can Do Bayesian Inference", the PFN paper☆227Updated 10 months ago
- Open-source framework for uncertainty and deep learning models in PyTorch☆424Updated this week
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 6 years ago
- Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty☆143Updated 2 years ago
- Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)☆78Updated 3 years ago
- Bayesianize: A Bayesian neural network wrapper in pytorch☆88Updated last year
- Public repo for course material on Bayesian machine learning at ENS Paris-Saclay and Univ Lille☆89Updated 6 months ago
- Example code of Sparse Gaussian Process Attention (ICLR 2023)☆25Updated last year
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆89Updated 5 years ago
- Our maintained PFN repository. Come here to train SOTA PFNs.☆102Updated 2 weeks ago