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 efficiently sampling functions from GP(flow) posteriors☆74Updated 5 years ago
- Code for Gaussian Score Matching Variational Inference☆35Updated 11 months ago
- A repository with implementations of major papers on Gaussian Process regression models, implemented from scratch in Python, notably incl…☆14Updated 3 years ago
- Code for the paper 'Neural Variational Gradient Descent'. We perform nonparametric variational inference by transporting samples along a …☆12Updated 4 years ago
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆25Updated 3 years ago
- [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)☆23Updated 5 years ago
- Exploring how to to deal with uncertain inputs with gaussian process regression models.☆27Updated 4 years ago
- ☆52Updated 2 years ago
- ☆31Updated 2 years ago
- ☆15Updated 3 years ago
- All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)☆20Updated 3 years ago
- A Tensorflow based library for Time Series Modelling with Gaussian Processes☆32Updated last year
- code for "Neural Conservation Laws A Divergence-Free Perspective".☆42Updated 3 years ago
- Lightweight MCMC sampling for PyTorch Models aka My Corona Project☆52Updated 6 months ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆21Updated 4 years ago
- An ultra-lightweight JAX implementation of sparse Gaussian processes via pathwise sampling.☆22Updated 4 years ago
- Computing gradients and Hessians of feed-forward networks with GPU acceleration☆20Updated last year
- Sampling with gradient-based Markov Chain Monte Carlo approaches☆109Updated last year
- A Metropolis-Hastings MCMC sampler accelerated via diffusion models☆16Updated last year
- Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)☆42Updated 3 years ago
- Flow Annealed Importance Sampling Bootstrap (FAB) with JAX.☆13Updated last year
- Refining continuous-in-depth neural networks☆42Updated 4 years ago
- Normalizing Flows with a resampled base distribution☆47Updated 3 years ago
- Public code for running Stochastic Gradient Descent on GPs.☆39Updated 9 months ago
- Efficient SDE samplers including Gaussian-based probabilistic solvers. Written in JAX.☆10Updated last year
- Neural likelihood-free methods in PyTorch.☆39Updated 6 years ago
- A community repository for benchmarking Bayesian methods☆12Updated 2 years ago
- ☆112Updated 4 years ago
- Code for the paper: Kernel Distributionally Robust Optimization☆13Updated 4 years ago
- Gradient-informed particle MCMC methods☆12Updated 2 years ago