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
  ☆52Updated 5 years ago
• federicobergamin / riemannian-laplace-approximation
  Repository for the paper "Riemannian Laplace approximations for Bayesian neural networks"
  ☆11Updated last year
• vdutor / neural-diffusion-processes
  Neural Diffusion Processes
  ☆81Updated last year
• vislearn / FFF
  Free-form flows are a generative model training a pair of neural networks via maximum likelihood
  ☆49Updated 4 months ago
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆75Updated 3 years 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
• martius-lab / beta-nll
  beta-NLL introduced in our paper "On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks" ICLR 2022
  ☆45Updated 3 years ago
• PetrMokrov / Large-Scale-Wasserstein-Gradient-Flows
  Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)
  ☆38Updated 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
• samholt / NeuralLaplace
  Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.
  ☆81Updated 6 months ago
• juntang-zhuang / TorchDiffEqPack
  ☆48Updated 2 years ago
• AaltoML / scalable-inference-in-sdes
  Methods and experiments for assumed density SDE approximations
  ☆12Updated 3 years ago
• pierreablin / ksddescent
  Kernel Stein Discrepancy Descent : a method to sample from unnormalized densities
  ☆22Updated last year
• AaltoML / PeriodicBNN
  Code for 'Periodic Activation Functions Induce Stationarity' (NeurIPS 2021)
  ☆19Updated 4 years ago
• cambridge-mlg / neural_diffusion_processes
  ☆13Updated 2 years ago
• anh-tong / fractional_neural_sde
  Fractional White Noises for Neural Stochastic Differential Equations (NeurIPS 2022)
  ☆16Updated 2 years ago
• facebookresearch / lagrangian-ot
  Neural Optimal Transport with Lagrangian Costs
  ☆60Updated 5 months ago
• PrincetonLIPS / AHGP
  [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)
  ☆23Updated 4 years ago
• andres-fr / skerch
  Sketched linear operations for PyTorch
  ☆96Updated last week
• f-dangel / curvlinops
  PyTorch linear operators for curvature matrices (Hessian, Fisher/GGN, KFAC, ...)
  ☆57Updated this week
• EmoryMLIP / OT-Flow
  PyTorch implementation of the OT-Flow approach in arXiv:2006.00104
  ☆56Updated last year
• mbilos / neural-flows-experiments
  Experiments for Neural Flows paper
  ☆98Updated 3 years ago
• tensorly / torch
  TensorLy-Torch: Deep Tensor Learning with TensorLy and PyTorch
  ☆81Updated last year
• alisiahkoohi / Langevin-dynamics
  Sampling with gradient-based Markov Chain Monte Carlo approaches
  ☆108Updated last year
• htlambley / functional_autoencoders
  Autoencoders in Function Space: Resolution-invariant autoencoders for functional data based on the functional variational autoencoder (FV…
  ☆28Updated 2 weeks ago
• sbyebss / variational_wgf
  [ICML2022] Variational Wasserstein gradient flow
  ☆24Updated 3 years ago
• activatedgeek / understanding-bayesian-classification
  On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
  ☆21Updated 3 years ago
• bobby-he / bayesian-ntk
  Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'
  ☆26Updated 4 years ago
• afqueiruga / StatefulOdeNets
  Refining continuous-in-depth neural networks
  ☆42Updated 3 years ago