Alternatives and similar repositories for GP-Derivatives-Variational-Inference

Users that are interested in GP-Derivatives-Variational-Inference are comparing it to the libraries listed below

• Jiacheng-Zhu-AIML / WGPOT
  The Wasserstein Distance and Optimal Transport Map of Gaussian Processes
  ☆52Updated 5 years ago
• NoemieJaquier / GaBOtorch
  This repository contains the source code to perform Geometry-aware Bayesian Optimization (GaBO) on Riemannian manifolds.
  ☆53Updated 3 years ago
• dgedon / DeepSSM_SysID
  Official PyTorch implementation of "Deep State Space Models for Nonlinear System Identification", 2020.
  ☆98Updated 3 years ago
• TeaPearce / Bayesian_NN_Ensembles
  AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'
  ☆90Updated 5 years ago
• activatedgeek / svgd
  PyTorch implementation of Stein Variational Gradient Descent
  ☆45Updated 2 years ago
• judelo / gmmot
  Python notebooks for Optimal Transport between Gaussian Mixture Models
  ☆46Updated 4 years ago
• wjmaddox / online_gp
  Code repo for "Kernel Interpolation for Scalable Online Gaussian Processes"
  ☆64Updated 4 years ago
• GalaxyFox / DS-GA-3001-Deep_Kalman_Filter
  This is a re-implementation and test on paper Deep Kalman Filter: https://arxiv.org/pdf/1511.05121.pdf
  ☆19Updated 5 years ago
• thangbui / streaming_sparse_gp
  Streaming sparse Gaussian process approximations
  ☆68Updated 3 years ago
• mbilos / neural-flows-experiments
  Experiments for Neural Flows paper
  ☆99Updated 3 years ago
• Physics-aware-AI / Symplectic-ODENet
  ☆47Updated 4 years ago
• Machine-Learning-Dynamical-Systems / kooplearn
  A Python package to learn the Koopman operator.
  ☆63Updated last week
• mpritzkoleit / deep-ensembles
  Reproduction of the paper: Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
  ☆26Updated 5 years ago
• cagatayyildiz / ODE2VAE
  ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
  ☆130Updated last year
• tonyduan / mixture-density-network
  Mixture density network implemented in PyTorch.
  ☆151Updated 2 years ago
• pmorenoz / HetMOGP
  Heterogeneous Multi-output Gaussian Processes
  ☆53Updated 5 years ago
• vgsatorras / hybrid-inference
  ☆21Updated 5 years ago
• Jiacheng-Zhu-AIML / Neural-Processes
  This repository contains PyTorch implementations of Neural Process, Attentive Neural Process, and Recurrent Attentive Neural Process.
  ☆19Updated 4 years ago
• AaltoML / sequential-gp
  Code for 'Memory-based dual Gaussian processes for sequential learning' (ICML 2023)
  ☆12Updated 2 years ago
• cagrell / gp_constr
  Python model for constrained GP
  ☆26Updated 4 years ago
• brain-research / nngp
  Deep neural network kernel for Gaussian process
  ☆212Updated 5 years ago
• fusion-ml / OCBO
  Offline Contextual Bayesian Optimization
  ☆14Updated 2 years ago
• ntienvu / KnownOptimum_BO
  Release code for ICML2020 Knowing The What But Not The Where in Bayesian Optimization
  ☆15Updated 2 years ago
• DanieleGammelli / DeepKalmanFilter
  Pyro/Pytorch implementation of Deep Kalman FIlter for shared-mobility demand prediction
  ☆48Updated 5 years ago
• DartML / Stein-Variational-Gradient-Descent
  code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"
  ☆100Updated 6 years ago
• jonasrothfuss / meta_learning_pacoh
  Meta-learning Gaussian process (GP) priors via PAC-Bayes bounds
  ☆26Updated last year
• j-wilson / GPflowSampling
  Code for efficiently sampling functions from GP(flow) posteriors
  ☆73Updated 4 years ago
• samholt / NeuralLaplace
  Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.
  ☆81Updated 6 months ago
• CampusAI / Hamiltonian-Generative-Networks
  Re-implementation of Hamiltonian Generative Networks paper
  ☆33Updated 3 years ago
• asikist / nnc
  A framework for neural network control of dynamical systems over graphs.
  ☆56Updated 3 years ago