Alternatives and similar repositories for neuralprocesses:

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

• cambridge-mlg / convcnp
  Implementation of the Convolutional Conditional Neural Process
  ☆121Updated 3 years ago
• markvdw / RobustGP
  Robust initialisation of inducing points in sparse variational GP regression models.
  ☆33Updated 2 years ago
• YannDubs / Neural-Process-Family
  Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.
  ☆213Updated 9 months ago
• jeremiecoullon / SGMCMCJax
  Lightweight library of stochastic gradient MCMC algorithms written in JAX.
  ☆102Updated last year
• secondmind-labs / GPflux
  Deep GPs built on top of TensorFlow/Keras and GPflow
  ☆124Updated 5 months ago
• jejjohnson / gp_model_zoo
  Literature and light wrappers for gaussian process models.
  ☆45Updated 3 years ago
• amzn / orthogonal-additive-gaussian-processes
  Light-weighted code for Orthogonal Additive Gaussian Processes
  ☆41Updated 8 months ago
• jmaronas / TGP.pytorch
  Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021
  ☆24Updated 2 years ago
• wesselb / lab
  A generic interface for linear algebra backends
  ☆73Updated last month
• probabilists / lampe
  Likelihood-free AMortized Posterior Estimation with PyTorch
  ☆126Updated 7 months ago
• danielward27 / flowjax
  ☆152Updated 3 weeks ago
• wesselb / gpar
  Implementation of the Gaussian Process Autoregressive Regression Model
  ☆63Updated 2 months ago
• jax-ml / bayeux
  State of the art inference for your bayesian models.
  ☆204Updated 3 months ago
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆72Updated 2 years ago
• j-wilson / GPflowSampling
  Code for efficiently sampling functions from GP(flow) posteriors
  ☆72Updated 4 years ago
• sbi-benchmark / sbibm
  Simulation-based inference benchmark
  ☆96Updated 2 months ago
• jfcrenshaw / pzflow
  Probabilistic modeling of tabular data with normalizing flows.
  ☆55Updated last month
• mackelab / simformer
  Combination of transformers and diffusion models for flexible all-in-one simulation-based inference
  ☆61Updated 9 months ago
• francois-rozet / inox
  Stainless neural networks in JAX
  ☆33Updated last week
• Information-Fusion-Lab-Umass / NuX
  Normalizing Flows using JAX
  ☆83Updated last year
• sbi-dev / pyknos
  Conditional density estimation with neural networks
  ☆30Updated 2 months ago
• VincentStimper / resampled-base-flows
  Normalizing Flows with a resampled base distribution
  ☆46Updated 2 years ago
• DanieleGammelli / pyro-torch-normalizing-flows
  Implementations of Normalizing Flows in Pytorch/Pyro
  ☆19Updated 4 years ago
• GaussianProcessesCambridge / meetup-resources
  Collection of resources from each meetup event
  ☆13Updated 4 years ago
• biweidai / SINF
  Sliced Iterative Generator (SIG) & Gaussianizing Iterative Slicing (GIS)
  ☆36Updated 2 years ago
• vr308 / Generalised-Gaussian-Processes
  Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods
  ☆22Updated last year
• conormdurkan / lfi
  Neural likelihood-free methods in PyTorch.
  ☆39Updated 5 years ago
• google-deepmind / annealed_flow_transport
  ☆50Updated 2 years ago
• desi-ivanova / idad
  Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods
  ☆19Updated 3 years ago
• tranbahien / you-need-a-good-prior
  All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)
  ☆21Updated 2 years ago