Alternatives and similar repositories for non-gaussian-gaussian-processes:

Users that are interested in non-gaussian-gaussian-processes are comparing it to the libraries listed below

• jmaronas / TGP.pytorch
  Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021
  ☆24Updated 2 years ago
• runame / laplace-refinement
  Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
  ☆10Updated 2 years ago
• ehoogeboom / convolution_exponential_and_sylvester
  ☆28Updated 3 years ago
• mj-will / augmented-flows
  Pytorch implementation of the basic idea presented in https://arxiv.org/abs/2002.07101
  ☆12Updated 5 years ago
• MJ10 / DEUP
  Code for experiments to learn uncertainty
  ☆30Updated 2 years ago
• christina-winkler / cnfs-super-resolution
  Master thesis for the MSc. Artificial Intelligence at the University of Amsterdam, 2019. Topic: Super-resolution with Conditional Normali…
  ☆20Updated 10 months ago
• chenlin9 / Gaussianization_Flows
  ☆22Updated 4 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
  ☆42Updated 3 years ago
• ratschlab / repulsive_ensembles
  Repo for our paper "Repulsive deep ensembles are Bayesian"
  ☆19Updated 3 years ago
• bobby-he / bayesian-ntk
  Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'
  ☆26Updated 4 years ago
• cambridge-mlg / cnn-gp
  Pytorch version of "Deep Convolutional Networks as shallow Gaussian Processes" by Adrià Garriga-Alonso, Carl Rasmussen and Laurence Aitch…
  ☆32Updated 4 years ago
• FrederikWarburg / LaplaceAE
  ☆32Updated 2 years ago
• noegroup / stochastic_normalizing_flows
  Stochastic Normalizing Flows
  ☆76Updated 3 years ago
• cambridge-mlg / convcnp
  Implementation of the Convolutional Conditional Neural Process
  ☆121Updated 3 years ago
• JeremiasKnoblauch / GVIPublic
  Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)
  ☆20Updated 6 years ago
• izmailovpavel / bnn_covariate_shift
  Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"
  ☆33Updated 2 years ago
• ratschlab / bnn_priors
  Code for the paper "Bayesian Neural Network Priors Revisited"
  ☆57Updated 3 years ago
• pierreablin / ksddescent
  Kernel Stein Discrepancy Descent : a method to sample from unnormalized densities
  ☆21Updated 11 months ago
• edebrouwer / sporadic_ccm
  Pytorch implementation of "Inferring Causal Dependencies between Chaotic Dynamical Systems from Sporadic Time Series"
  ☆9Updated 4 years ago
• volagold / nce
  Noise Contrastive Estimation (NCE) in PyTorch
  ☆32Updated last month
• blt2114 / CDE_with_BNF
  Toy Examples of Conditional Density Estimation with Bayesian Normalizing flows
  ☆22Updated 6 years ago
• SamsungLabs / semi-supervised-NFs
  Code for the paper Semi-Conditional Normalizing Flows for Semi-Supervised Learning
  ☆28Updated 3 years ago
• timrudner / S-FSVI
  Code for the paper 'Continual Learning via Sequential Function-Space Variational Inference'
  ☆23Updated 2 years ago
• AMLab-Amsterdam / FNP
  Implementation of the Functional Neural Process models
  ☆43Updated 4 years ago
• ssydasheng / FBNN
  Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)
  ☆82Updated 4 years ago
• ermongroup / f-dre
  Featurized Density Ratio Estimation
  ☆20Updated 3 years ago
• ebonilla / gaussianprocesses
  Modern Gaussian Processes: Scalable Inference and Novel Applications
  ☆20Updated 5 years ago
• team-approx-bayes / dnn2gp
  Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)
  ☆48Updated 5 years ago
• samuelstanton / conformal-bayesopt
  Bayesian optimization with conformal coverage guarantees
  ☆27Updated 2 years ago
• AnthonyRentsch / calibrated_regression
  A tutorial for the 2018 paper Accurate Uncertainties for Deep Learning Using Calibrated Regression by Kuleshov et al.
  ☆50Updated 5 years ago