TyXe-BDL / TyXeLinks
☆156Updated 3 years ago
Alternatives and similar repositories for TyXe
Users that are interested in TyXe are comparing it to the libraries listed below
Sorting:
- PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks☆464Updated last year
- Deep GPs built on top of TensorFlow/Keras and GPflow☆128Updated last year
- Official Implementation of "Transformers Can Do Bayesian Inference", the PFN paper☆251Updated last year
- Light-weighted code for Orthogonal Additive Gaussian Processes☆44Updated last year
- Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.☆241Updated 2 years ago
- Lightweight library of stochastic gradient MCMC algorithms written in JAX.☆105Updated 2 years ago
- Code for the paper "Bayesian Neural Network Priors Revisited"☆60Updated 4 years ago
- Materials of the Nordic Probabilistic AI School 2022.☆181Updated 3 years ago
- A library for uncertainty quantification based on PyTorch☆121Updated 4 years ago
- Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.☆227Updated last year
- Regression datasets from the UCI repository with standardized test-train splits.☆52Updated 3 years ago
- Robust initialisation of inducing points in sparse variational GP regression models.☆34Updated 3 years ago
- Laplace Redux -- Effortless Bayesian Deep Learning☆44Updated 7 months ago
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆25Updated 3 years ago
- Python and torch-based package implementing various parametric and nonparametric methods for conditional density estimation☆197Updated last month
- A community repository for benchmarking Bayesian methods☆112Updated 4 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆90Updated 5 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆74Updated 5 years ago
- Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances☆50Updated 2 years ago
- Multi-Output Gaussian Process Toolkit☆183Updated 7 months ago
- Gaussian process modelling in Python☆226Updated last year
- Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"☆172Updated 3 years ago
- Simple (and cheap!) neural network uncertainty estimation☆80Updated 3 months ago
- A general-purpose, deep learning-first library for constrained optimization in PyTorch☆152Updated 2 months ago
- A framework for composing Neural Processes in Python☆89Updated last year
- Bayesian Neural Network Surrogates for Bayesian Optimization☆67Updated last year
- Implementation of random Fourier features for kernel method, like support vector machine and Gaussian process model☆103Updated last year
- ☆251Updated 3 years ago
- Recursive Bayesian Estimation (Sequential / Online Inference)☆61Updated last year
- Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)☆42Updated 4 years ago