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:
- Neural Diffusion Processes☆81Updated last year
- The Wasserstein Distance and Optimal Transport Map of Gaussian Processes☆53Updated 5 years ago
- Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)☆37Updated 3 years ago
- Repository for the paper "Riemannian Laplace approximations for Bayesian neural networks"☆11Updated last year
- Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.☆81Updated 5 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 2 years ago
- [ICML2022] Variational Wasserstein gradient flow☆24Updated 2 years ago
- Free-form flows are a generative model training a pair of neural networks via maximum likelihood☆48Updated 3 months ago
- Mutual information estimators and benchmark☆53Updated 3 weeks ago
- Pytorch implementation of "Entropic Neural Optimal Transport via Diffusion Processes" (NeurIPS 2023, oral).☆38Updated last year
- IVON optimizer for neural networks based on variational learning.☆72Updated 11 months ago
- PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization" (ICLR 2021)☆24Updated 3 years ago
- All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)☆20Updated 3 years ago
- Laplace Redux -- Effortless Bayesian Deep Learning☆44Updated 4 months ago
- Sampling with gradient-based Markov Chain Monte Carlo approaches☆108Updated last year
- Methods and experiments for assumed density SDE approximations☆12Updated 3 years ago
- NeurIPS'23: Energy Discrepancies: A Score-Independent Loss for Energy-Based Models☆16Updated 11 months ago
- Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".☆36Updated 3 years ago
- beta-NLL introduced in our paper "On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks" ICLR 2022☆46Updated 3 years ago
- Official implementation of Deep Momentum Schrödinger Bridge☆28Updated last year
- Official PyTorch implementation of NeuralSVD (ICML 2024)☆20Updated last year
- Featurized Density Ratio Estimation☆20Updated 4 years ago
- Official code for the ICLR 2021 paper Neural ODE Processes☆75Updated 3 years ago
- ☆13Updated 2 years ago
- Official implementation of Transformer Neural Processes☆78Updated 3 years ago
- ☆24Updated 3 years ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 4 years ago
- Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks☆10Updated 2 years ago
- Refining continuous-in-depth neural networks☆42Updated 3 years ago
- A simple pytorch implementation of Langevin Monte Carlo algorithms.☆53Updated 5 years ago