AaltoML / sequential-gpLinks
Code for 'Memory-based dual Gaussian processes for sequential learning' (ICML 2023)
☆12Updated 2 years ago
Alternatives and similar repositories for sequential-gp
Users that are interested in sequential-gp are comparing it to the libraries listed below
Sorting:
- The Wasserstein Distance and Optimal Transport Map of Gaussian Processes☆52Updated 5 years ago
- Repository for the paper "Riemannian Laplace approximations for Bayesian neural networks"☆12Updated 2 years ago
- Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.☆82Updated 9 months ago
- The companion code for the paper "Variational inference via Wasserstein gradient flows (W-VI) M. Lambert, S. Chewi, F. Bach, S. Bonnabel…☆14Updated 3 years ago
- Methods and experiments for assumed density SDE approximations☆12Updated 4 years ago
- Neural Diffusion Processes☆82Updated last year
- Mutual information estimators and benchmark☆56Updated 4 months ago
- Sketched linear operations for PyTorch☆100Updated 3 months ago
- Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)☆42Updated 3 years ago
- Official code for the ICLR 2021 paper Neural ODE Processes☆76Updated 3 years ago
- IVON optimizer for neural networks based on variational learning.☆81Updated last year
- PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization" (ICLR 2021)☆33Updated 3 years ago
- NeurIPS'23: Energy Discrepancies: A Score-Independent Loss for Energy-Based Models☆17Updated last year
- All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)☆20Updated 3 years ago
- JAX implementation of the JKOnet* architecture presented in "Learning Diffusion at Lightspeed".☆38Updated 10 months ago
- Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".☆37Updated 3 years ago
- Sampling with gradient-based Markov Chain Monte Carlo approaches☆109Updated last year
- Official implementation of Transformer Neural Processes☆78Updated 3 years ago
- Code for 'Periodic Activation Functions Induce Stationarity' (NeurIPS 2021)☆19Updated 4 years ago
- A repository with implementations of major papers on Gaussian Process regression models, implemented from scratch in Python, notably incl…☆14Updated 3 years ago
- [ICML2022] Variational Wasserstein gradient flow☆24Updated 3 years ago
- ☆25Updated 3 years ago
- Official PyTorch implementation of NeuralSVD (ICML 2024)☆22Updated last year
- Model hub for all your DiffeqML needs. Pretrained weights, modules, and basic inference infrastructure☆28Updated 2 years ago
- ☆13Updated 2 years ago
- Code for "Log Neural Controlled Differential Equations" (ICML 2024) and "Structured Linear CDEs" (NeurIPS 2025, Spotlight)☆29Updated last week
- Neural Optimal Transport with Lagrangian Costs☆61Updated 8 months ago
- ☆33Updated 3 years ago
- PyTorch linear operators for curvature matrices (Hessian, Fisher/GGN, KFAC, ...)☆62Updated this week
- Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks☆10Updated 3 years ago