Alternatives and similar repositories for sequential-gp

Users that are interested in sequential-gp are comparing it to the libraries listed below

• marc-h-lambert / W-VI
  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
• Jiacheng-Zhu-AIML / WGPOT
  The Wasserstein Distance and Optimal Transport Map of Gaussian Processes
  ☆53Updated 5 years ago
• federicobergamin / riemannian-laplace-approximation
  Repository for the paper "Riemannian Laplace approximations for Bayesian neural networks"
  ☆11Updated last year
• PetrMokrov / Large-Scale-Wasserstein-Gradient-Flows
  Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)
  ☆35Updated 3 years ago
• vdutor / neural-diffusion-processes
  Neural Diffusion Processes
  ☆81Updated last year
• cbg-ethz / bmi
  Mutual information estimators and benchmark
  ☆51Updated 7 months ago
• team-approx-bayes / ivon
  IVON optimizer for neural networks based on variational learning.
  ☆70Updated 9 months ago
• iamalexkorotin / Wasserstein2Barycenters
  PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization" (ICLR 2021)
  ☆24Updated 3 years ago
• samholt / NeuralLaplace
  Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.
  ☆80Updated 4 months ago
• tranbahien / you-need-a-good-prior
  All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)
  ☆20Updated 3 years ago
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆74Updated 3 years ago
• plugyawn / gp-zoo
  A repository with implementations of major papers on Gaussian Process regression models, implemented from scratch in Python, notably incl…
  ☆14Updated 2 years ago
• martius-lab / beta-nll
  beta-NLL introduced in our paper "On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks" ICLR 2022
  ☆46Updated 3 years ago
• Sanaelotfi / Bayesian_model_comparison
  Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".
  ☆36Updated 3 years ago
• PrincetonLIPS / AHGP
  [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)
  ☆22Updated 4 years ago
• J-zin / energy-discrepancy
  NeurIPS'23: Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
  ☆17Updated 10 months ago
• vislearn / FFF
  Free-form flows are a generative model training a pair of neural networks via maximum likelihood
  ☆47Updated 2 months ago
• mbilos / neural-flows-experiments
  Experiments for Neural Flows paper
  ☆97Updated 3 years ago
• sbyebss / variational_wgf
  [ICML2022] Variational Wasserstein gradient flow
  ☆23Updated 2 years ago
• runame / laplace-refinement
  Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
  ☆10Updated 2 years ago
• tung-nd / TNP-pytorch
  Official implementation of Transformer Neural Processes
  ☆78Updated 3 years ago
• FrederikWarburg / LaplaceAE
  ☆32Updated 3 years ago
• cambridge-mlg / neural_diffusion_processes
  ☆13Updated 2 years ago
• hmdolatabadi / LRS_NF
  [AISTATS2020] The official repository of "Invertible Generative Modling using Linear Rational Splines (LRS)".
  ☆20Updated 2 years ago
• ermongroup / f-dre
  Featurized Density Ratio Estimation
  ☆20Updated 4 years ago
• f-dangel / curvlinops
  PyTorch linear operators for curvature matrices (Hessian, Fisher/GGN, KFAC, ...)
  ☆41Updated this week
• activatedgeek / understanding-bayesian-classification
  On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
  ☆21Updated 3 years ago
• vdeborto / cdsb
  ☆24Updated 3 years ago
• dngfra / repulsive_ensembles
  Repo for our paper "Repulsive deep ensembles are Bayesian"
  ☆19Updated 3 years ago
• amzn / orthogonal-additive-gaussian-processes
  Light-weighted code for Orthogonal Additive Gaussian Processes
  ☆43Updated last year