zalandoresearch / JaxRK
RKHS feature vectors, operators, and statistical models using JAX for automatic differentiation
☆8Updated 3 years ago
Alternatives and similar repositories for JaxRK:
Users that are interested in JaxRK are comparing it to the libraries listed below
- A lightweight didactic library of kernel methods using the back-end JAX.☆12Updated last year
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 4 years ago
- An ultra-lightweight JAX implementation of sparse Gaussian processes via pathwise sampling.☆22Updated 3 years ago
- ☆49Updated 2 years ago
- Sequential Neural Likelihood☆39Updated 5 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆70Updated 4 years ago
- Lightweight library of stochastic gradient MCMC algorithms written in JAX.☆101Updated last year
- Riemannian Convex Potential Maps☆67Updated last year
- Minimal Gaussian process library in JAX with a simple (custom) approach to state management.☆11Updated last year
- A generic interface for linear algebra backends☆71Updated 8 months ago
- Neural likelihood-free methods in PyTorch.☆39Updated 5 years ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 6 years ago
- Bayesian learning and inference for state space models (SSMs) using Google Research's JAX as a backend☆58Updated 8 months ago
- Implementation of stochastic variational inference for differentially deep gaussian processes☆15Updated 6 years ago
- Normalizing Flows using JAX☆82Updated last year
- This is a collection of code samples aimed at illustrating temporal parallelization methods for sequential data.☆31Updated last year
- ☆30Updated 2 years ago
- Exponential families for JAX☆63Updated this week
- Deterministic particle dynamics for simulating Fokker-Planck probability flows☆24Updated last year
- Layered distributions using FLAX/JAX☆10Updated 4 years ago
- ☆80Updated 3 years ago
- Conditional density estimation with neural networks☆30Updated last month
- Bayesian inference with Python and Jax.☆32Updated 2 years ago
- Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX☆98Updated last year
- Python and MATLAB code for Stein Variational sampling methods☆24Updated 5 years ago
- [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)☆21Updated 4 years ago
- Sparse Orthogonal Variational Inference for Gaussian Processes (SOLVE-GP)☆22Updated 3 years ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆18Updated 3 years ago
- ☆22Updated 4 years ago