Alternatives and similar repositories for MV-Kumaraswamy:

Users that are interested in MV-Kumaraswamy are comparing it to the libraries listed below

• cunningham-lab / igr
  ☆30Updated 4 years ago
• MichaelArbel / GeneralizedEBM
  ☆53Updated 8 months ago
• LuisArmandoPerez / DiffusionVAE
  ☆29Updated 3 years ago
• ermongroup / f-dre
  Featurized Density Ratio Estimation
  ☆20Updated 3 years ago
• cunningham-lab / cb_and_cc
  ☆36Updated 2 years ago
• EmoryMLIP / OT-Flow
  PyTorch implementation of the OT-Flow approach in arXiv:2006.00104
  ☆51Updated 8 months ago
• lxuechen / BDMC
  PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.
  ☆52Updated 3 years ago
• yperugachidiaz / invertible_densenets
  ☆24Updated 4 years ago
• joeybose / HyperbolicNF
  ICML 2020 Paper: Latent Variable Modelling with Hyperbolic Normalizing Flows
  ☆53Updated 2 years ago
• sungyubkim / amortized_svgd
  A pytorch implementation of Amortized Stein Variational Gradient Descent/ Stein GAN
  ☆18Updated 6 years ago
• bunnech / gwgan
  Learning Generative Models across Incomparable Spaces (ICML 2019)
  ☆27Updated 5 years ago
• dimkal89 / rvae
  ☆38Updated 4 years ago
• choidami / sst
  ☆49Updated 4 years ago
• sajadn / Exemplar-VAE
  Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation
  ☆69Updated 4 years ago
• xinshi-chen / ParticleFlowBayesRule
  Pytorch implementation for "Particle Flow Bayes' Rule"
  ☆14Updated 5 years ago
• franrruiz / augment-reduce
  Code for Augment & Reduce, a scalable stochastic algorithm for large categorical distributions
  ☆10Updated 6 years ago
• larsmaaloee / BIVA
  BIVA: A Very Deep Hierarchy of Latent Variables forGenerative Modeling
  ☆29Updated 5 years ago
• AVMCMC / AuxiliaryVariationalMCMC
  Code required to reproduce the experiments in Auxiliary Variational MCMC
  ☆17Updated 6 years ago
• noahgolmant / SGLD
  Stochastic Gradient Langevin Dynamics for Bayesian learning
  ☆31Updated 3 years ago
• wgrathwohl / LSD
  Official Release of "Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling"
  ☆49Updated 4 years ago
• mfederici / dsit
  Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"
  ☆25Updated 3 years ago
• team-approx-bayes / dnn2gp
  Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)
  ☆48Updated 5 years ago
• franciscovargas / GP_Sinkhorn
  GP Sinkhorn Implementation, paper: https://www.mdpi.com/1099-4300/23/9/1134
  ☆22Updated 2 years ago
• izmailovpavel / bnn_covariate_shift
  Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"
  ☆33Updated 2 years ago
• gwgundersen / rflvm
  Random feature latent variable models in Python
  ☆22Updated last year
• AMLab-Amsterdam / FNP
  Implementation of the Functional Neural Process models
  ☆43Updated 4 years ago
• plai-group / tvo
  Code for the Thermodynamic Variational Objective
  ☆26Updated 2 years ago
• AllanYangZhou / metalearning-symmetries
  Experiments for Meta-Learning Symmetries by Reparameterization
  ☆56Updated 4 years ago
• wellecks / vaes
  Variational Autoencoders & Normalizing Flows Project
  ☆18Updated 8 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 5 years ago