AaltoML / boundary-gpView external linksLinks
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features
☆24Apr 20, 2019Updated 6 years ago
Alternatives and similar repositories for boundary-gp
Users that are interested in boundary-gp are comparing it to the libraries listed below
Sorting:
- Codes for Hilbert space reduced-rank GP regression☆15Jul 30, 2019Updated 6 years ago
- Deep Gaussian Processes with Importance-Weighted Variational Inference☆39May 30, 2019Updated 6 years ago
- Supplementary code for the NeurIPS 2020 paper "Matern Gaussian processes on Riemannian manifolds".☆30Feb 3, 2025Updated last year
- Preconditioning Kernel Matrices☆15Jun 9, 2016Updated 9 years ago
- Unifying sparse approximations for Gaussian process regression and classification, using Power EP☆22Oct 17, 2016Updated 9 years ago
- Efficient SDE samplers including Gaussian-based probabilistic solvers. Written in JAX.☆10Feb 8, 2025Updated last year
- End-to-End Probabilistic Inference for Nonstationary Audio Analysis☆12Aug 7, 2019Updated 6 years ago
- UAI paper 'Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions'☆11Jun 26, 2019Updated 6 years ago
- Non-Parametric Calibration for Classification (AISTATS 2020)☆19Feb 3, 2022Updated 4 years ago
- An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval☆12Feb 26, 2019Updated 6 years ago
- Personal experiments and implementation examples of deep learning papers.☆11Jan 23, 2018Updated 8 years ago
- A collection of Gaussian process models☆30Aug 17, 2017Updated 8 years ago
- Gaussian processes regression models with linear inequality constraints☆15Jul 10, 2024Updated last year
- Non-stationary spectral mixture kernels implemented in GPflow☆28Nov 30, 2018Updated 7 years ago
- ☆11Dec 9, 2017Updated 8 years ago
- Implementation of MAML in numpy, deriving gradients and implementing backprop manually☆14Nov 15, 2018Updated 7 years ago
- An integrated demo: Gaussian processes for PDEs and inverse problems☆16Jul 17, 2025Updated 6 months ago
- Stochastic variational heteroscedastic Gaussian process☆15Mar 29, 2019Updated 6 years ago
- Gaussian process modeling in R☆16Nov 20, 2022Updated 3 years ago
- Unifying probabilistic models for time-frequency analysis. Spectral Mixture Gaussian processes in state space form.☆19May 30, 2019Updated 6 years ago
- ☆18Dec 31, 2018Updated 7 years ago
- Implementation of the Gaussian processes regression with inducing points for online data with ensemble Kalman filter estimation. Code for…☆17Jul 9, 2018Updated 7 years ago
- Public code for running Stochastic Gradient Descent on GPs.☆39Apr 23, 2025Updated 9 months ago
- Gaussian process time-frequency analysis toy examples☆17Apr 5, 2019Updated 6 years ago
- Bayesian optimization with Standard Gaussian Processes on high dimensional benchmarks☆19Jun 29, 2025Updated 7 months ago
- A framework for detecting misreported returns in hedge funds.☆16Aug 25, 2019Updated 6 years ago
- ☆40Apr 29, 2019Updated 6 years ago
- A tensorflow implementation of the NIPS 2018 paper "Variational Inference with Tail-adaptive f-Divergence"☆20Jan 11, 2019Updated 7 years ago
- PAC-Bayes generalization certificates for ICP☆21Nov 16, 2023Updated 2 years ago
- Deep GPs built on top of TensorFlow/Keras and GPflow☆128Oct 16, 2024Updated last year
- A variational method for fast, approximate inference for stochastic differential equations.☆45Feb 3, 2026Updated last week
- ☆25Jan 2, 2019Updated 7 years ago
- Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"☆27Mar 29, 2019Updated 6 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆42Nov 29, 2022Updated 3 years ago
- Code for the paper "Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluations"☆32Dec 4, 2025Updated 2 months ago
- A library implementing the kernels for and experiments using extrinsic gauge equivariant vector field Gaussian Processes☆26Oct 28, 2021Updated 4 years ago
- Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"☆28Feb 11, 2021Updated 5 years ago
- Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'☆62Jun 3, 2018Updated 7 years ago
- Code for the NeurIPS 2020 paper: "Federated Bayesian Optimization via Thompson Sampling"☆26Nov 6, 2020Updated 5 years ago