kd383 / GPML_SLDLinks
Scalable Log Determinants for Gaussian Process Kernel Learning (https://arxiv.org/abs/1711.03481) (NIPS 2017)
☆18Updated 7 years ago
Alternatives and similar repositories for GPML_SLD
Users that are interested in GPML_SLD are comparing it to the libraries listed below
Sorting:
- Matlab code implementing Minimum Probability Flow Learning.☆69Updated 10 years ago
- ☆51Updated last year
- Reducing Reparameterization Gradient Variance code.☆33Updated 8 years ago
- This repository houses the code for the community website http://www.probabilistic-numerics.org☆35Updated 5 years ago
- Stochastic Gradient Riemannian Langevin Dynamics☆34Updated 10 years ago
- Code for NIPS 2015 "Gradient-Free Hamiltonian Monte Carlo via Effecient Kernel Exponential Families"☆25Updated 7 years ago
- TensorFlow implementation for training MCMC samplers from the paper: Generalizing Hamiltonian Monte Carlo with Neural Network☆183Updated 6 years ago
- Entropy Search for Information-Efficient Global Optimization - JMLR v13☆30Updated 8 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 7 years ago
- Bayesian optimization in high-dimensions via random embedding.☆114Updated 12 years ago
- ☆26Updated 7 years ago
- Layered distributions using FLAX/JAX☆10Updated 4 years ago
- Look Ahead Hamiltonian Monte Carlo☆30Updated 10 years ago
- Prototypes of differentiable differential equation solvers in JAX.☆27Updated 5 years ago
- Code for density estimation with nonparametric cluster shapes.☆39Updated 9 years ago
- Code release for the ICLR paper☆21Updated 7 years ago
- Code repo for "Function-Space Distributions over Kernels"☆32Updated 4 years ago
- NeurIPS 2018. Linear-time model comparison tests.☆18Updated 5 years ago
- Code for "Efficient optimization of loops and limits with randomized telescoping sums"☆28Updated 6 years ago
- Deep convolutional gaussian processes.☆80Updated 5 years ago
- "Parameter origami" -- folding and unfolding collections of parameters for optimization and sensitivity analysis.☆14Updated last year
- Autoregressive Energy Machines☆78Updated 2 years ago
- NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.☆67Updated 6 years ago
- a deep recurrent model for exchangeable data☆34Updated 5 years ago
- PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.☆52Updated 4 years ago
- Jax-based MaxEnt☆17Updated 5 years ago
- Repo for a paper about constructing priors on very deep models.☆73Updated 9 years ago
- Code to minimize the Variational Contrastive Divergence (VCD)☆29Updated 6 years ago
- Discrete Object Generation with Reversible Inductive Construction (NeurIPS 2019)☆31Updated 4 years ago
- Practice with MCMC methods and dynamics (Langevin, Hamiltonian, etc.)☆42Updated 5 years ago