fidero / gp-derivativeLinks
Example code to speed up GP inference with gradients for high-dimensional inputs.
☆14Updated 4 years ago
Alternatives and similar repositories for gp-derivative
Users that are interested in gp-derivative are comparing it to the libraries listed below
Sorting:
- Code for Gaussian Score Matching Variational Inference☆34Updated 5 months ago
- [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)☆22Updated 4 years ago
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆24Updated 2 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆72Updated 4 years ago
- ☆15Updated 2 years ago
- A repository with implementations of major papers on Gaussian Process regression models, implemented from scratch in Python, notably incl…☆14Updated 2 years ago
- All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)☆20Updated 3 years ago
- Stainless neural networks in JAX☆34Updated 3 weeks ago
- ☆52Updated 2 years ago
- code for "Neural Conservation Laws A Divergence-Free Perspective".☆39Updated 2 years ago
- Efficient SDE samplers including Gaussian-based probabilistic solvers. Written in JAX.☆10Updated 6 months ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆20Updated 3 years ago
- A Tensorflow based library for Time Series Modelling with Gaussian Processes☆32Updated last year
- Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)☆34Updated 3 years ago
- Minimal JAX implementation of k-nearest neighbors using a k-d tree.☆48Updated 3 weeks ago
- ☆27Updated last year
- Sampling with gradient-based Markov Chain Monte Carlo approaches☆107Updated last year
- Normalizing Flows using JAX☆84Updated last year
- Neural likelihood-free methods in PyTorch.☆39Updated 5 years ago
- Agustinus' very opiniated publication-ready plotting library☆67Updated 3 months ago
- Code for the paper 'Neural Variational Gradient Descent'. We perform nonparametric variational inference by transporting samples along a …☆10Updated 4 years ago
- Normalizing Flows with a resampled base distribution☆47Updated 2 years ago
- Nonparametric Differential Equation Modeling☆54Updated last year
- Flow Annealed Importance Sampling Bootstrap (FAB) with JAX.☆11Updated last year
- Differentiable Principal Component Analysis (PCA) implementation in JAX☆29Updated 3 months ago
- Lightweight library of stochastic gradient MCMC algorithms written in JAX.☆104Updated last year
- Likelihood-free AMortized Posterior Estimation with PyTorch☆130Updated 11 months ago
- A LinearOperator implementation to wrap the numerical nuts and bolts of GPyTorch☆112Updated 4 months ago
- Deep GPs built on top of TensorFlow/Keras and GPflow☆127Updated 9 months ago
- Simulation-based inference in JAX☆33Updated 3 months ago