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"
☆58Updated 2 years ago
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:
- Large-scale uncertainty benchmark in deep learning.☆65Updated 7 months ago
- Simple (and cheap!) neural network uncertainty estimation☆78Updated 2 months ago
- Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control☆72Updated last year
- Bayesian active learning with EPIG data acquisition☆35Updated 3 months ago
- Mutual information estimators and benchmark☆55Updated 2 months ago
- This repository contains a Jax implementation of conformal training corresponding to the ICLR'22 paper "learning optimal conformal classi…☆130Updated 3 years ago
- Laplace approximations for Deep Learning.☆529Updated 7 months ago
- Laplace Redux -- Effortless Bayesian Deep Learning☆44Updated 6 months ago
- PyTorch linear operators for curvature matrices (Hessian, Fisher/GGN, KFAC, ...)☆60Updated 3 weeks ago
- IVON optimizer for neural networks based on variational learning.☆76Updated last year
- Code for the paper "Bayesian Neural Network Priors Revisited"☆58Updated 4 years ago
- Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".☆115Updated 3 years ago
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 3 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 6 years ago
- All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)☆20Updated 3 years ago
- Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, ca…☆177Updated last year
- Bayesianize: A Bayesian neural network wrapper in pytorch☆90Updated last year
- Agustinus' very opiniated publication-ready plotting library☆69Updated 7 months ago
- ☆251Updated 2 years ago
- Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty☆145Updated 2 years ago
- Sampling with gradient-based Markov Chain Monte Carlo approaches☆108Updated last year
- A package for conformal prediction with conditional guarantees.☆67Updated 2 months ago
- Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)☆78Updated 3 years ago
- Materials of the Nordic Probabilistic AI School 2023.☆91Updated 2 years ago
- Code for PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization, NeurIPS 2022☆17Updated 3 years ago
- Materials of the Nordic Probabilistic AI School 2022.☆181Updated 3 years ago
- Open-source framework for uncertainty and deep learning models in PyTorch☆456Updated last month
- Parameter-Free Optimizers for Pytorch☆130Updated last year
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆24Updated 2 years ago
- Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".☆37Updated 3 years ago