Alternatives and similar repositories for neuralprocesses

Users that are interested in neuralprocesses are comparing it to the libraries listed below

• YannDubs / Neural-Process-Family
  Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.
  ☆226Updated last year
• jmaronas / TGP.pytorch
  Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021
  ☆24Updated 2 years ago
• jejjohnson / gp_model_zoo
  Literature and light wrappers for gaussian process models.
  ☆48Updated 4 years ago
• secondmind-labs / GPflux
  Deep GPs built on top of TensorFlow/Keras and GPflow
  ☆127Updated last year
• cambridge-mlg / convcnp
  Implementation of the Convolutional Conditional Neural Process
  ☆125Updated 4 years ago
• amzn / orthogonal-additive-gaussian-processes
  Light-weighted code for Orthogonal Additive Gaussian Processes
  ☆43Updated last year
• AaltoML / spatio-temporal-GPs
  Code for NeurIPS 2021 paper 'Spatio-Temporal Variational Gaussian Processes'
  ☆47Updated 3 years ago
• TyXe-BDL / TyXe
  ☆154Updated 3 years ago
• probabilists / lampe
  Likelihood-free AMortized Posterior Estimation with PyTorch
  ☆131Updated last year
• tranbahien / you-need-a-good-prior
  All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)
  ☆20Updated 3 years ago
• wesselb / stheno
  Gaussian process modelling in Python
  ☆224Updated 10 months ago
• jeremiecoullon / SGMCMCJax
  Lightweight library of stochastic gradient MCMC algorithms written in JAX.
  ☆105Updated 2 years ago
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆75Updated 3 years ago
• AaltoML / BayesNewton
  Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.
  ☆239Updated last year
• VincentStimper / resampled-base-flows
  Normalizing Flows with a resampled base distribution
  ☆47Updated 3 years ago
• secondmind-labs / markovflow
  A Tensorflow based library for Time Series Modelling with Gaussian Processes
  ☆32Updated last year
• j-wilson / GPflowSampling
  Code for efficiently sampling functions from GP(flow) posteriors
  ☆73Updated 4 years ago
• dirmeier / ramsey
  Probabilistic deep learning using JAX
  ☆15Updated 8 months ago
• stefanosele / GPfY
  Gaussian processes with spherical harmonic features in JAX
  ☆15Updated 2 months ago
• anh-tong / signax
  Signax: Signature computation in JAX
  ☆36Updated 8 months ago
• schmidtjonathan / Climate2Weather
  This code accompanies the manuscript "A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulati…
  ☆25Updated 3 months ago
• GaussianProcessesCambridge / meetup-resources
  Collection of resources from each meetup event
  ☆13Updated 4 years ago
• DanieleGammelli / pyro-torch-normalizing-flows
  Implementations of Normalizing Flows in Pytorch/Pyro
  ☆18Updated 5 years ago
• ludwigwinkler / pytorch_MCMC
  Lightweight MCMC sampling for PyTorch Models aka My Corona Project
  ☆49Updated 2 months ago
• cornellius-gp / linear_operator
  A LinearOperator implementation to wrap the numerical nuts and bolts of GPyTorch
  ☆115Updated 3 weeks ago
• wesselb / lab
  A generic interface for linear algebra backends
  ☆74Updated 7 months ago
• jejjohnson / uncertain_gps
  Exploring how to to deal with uncertain inputs with gaussian process regression models.
  ☆27Updated 4 years ago
• danielward27 / flowjax
  ☆192Updated this week
• google-deepmind / annealed_flow_transport
  ☆51Updated 2 years ago
• Information-Fusion-Lab-Umass / NuX
  Normalizing Flows using JAX
  ☆85Updated last year