tranbahien / you-need-a-good-priorLinks
All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)
☆20Updated 3 years ago
Alternatives and similar repositories for you-need-a-good-prior
Users that are interested in you-need-a-good-prior are comparing it to the libraries listed below
Sorting:
- Repo for our paper "Repulsive deep ensembles are Bayesian"☆19Updated 4 years ago
- Sampling with gradient-based Markov Chain Monte Carlo approaches☆108Updated last year
- Improving predictions of Bayesian neural nets via local linearization, AISTATS 2021☆16Updated 3 years ago
- Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks☆10Updated 3 years ago
- IVON optimizer for neural networks based on variational learning.☆80Updated last year
- ☆15Updated 3 years ago
- Neural Diffusion Processes☆81Updated last year
- Official implementation of Transformer Neural Processes☆78Updated 3 years ago
- [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)☆23Updated 5 years ago
- Official code for the ICLR 2021 paper Neural ODE Processes☆75Updated 3 years ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆21Updated 4 years ago
- Lightweight MCMC sampling for PyTorch Models aka My Corona Project☆52Updated 5 months ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 6 years ago
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆25Updated 3 years ago
- Implementations of Normalizing Flows in Pytorch/Pyro☆18Updated 5 years ago
- Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)☆40Updated 3 years ago
- Laplace Redux -- Effortless Bayesian Deep Learning☆43Updated 7 months ago
- This repository contains code for applying Riemannian geometry in machine learning.☆78Updated 4 years ago
- Featurized Density Ratio Estimation☆20Updated 4 years ago
- Normalizing Flows with a resampled base distribution☆47Updated 3 years ago
- Simple (and cheap!) neural network uncertainty estimation☆79Updated 3 months ago
- Repository for the paper "Riemannian Laplace approximations for Bayesian neural networks"☆12Updated 2 years ago
- Example code to speed up GP inference with gradients for high-dimensional inputs.☆14Updated 4 years ago
- Example code of Sparse Gaussian Process Attention (ICLR 2023)☆26Updated 3 months ago
- The Wasserstein Distance and Optimal Transport Map of Gaussian Processes☆52Updated 5 years ago
- A simple pytorch implementation of Langevin Monte Carlo algorithms.☆53Updated 5 years ago
- Light-weighted code for Orthogonal Additive Gaussian Processes☆43Updated last year
- Code for Gaussian Score Matching Variational Inference☆35Updated 10 months ago
- Bayesian active learning with EPIG data acquisition☆35Updated 4 months ago
- beta-NLL introduced in our paper "On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks" ICLR 2022☆46Updated 3 years ago