Alternatives and similar repositories for large-scale-OT-mapping-TF

Users that are interested in large-scale-OT-mapping-TF are comparing it to the libraries listed below

• skolouri / swae
  Implementation of the Sliced Wasserstein Autoencoders
  ☆91Updated 7 years ago
• skolouri / swgmm
  Sliced Wasserstein Distance for Learning Gaussian Mixture Models
  ☆63Updated 2 years ago
• gpeyre / 2016-ICML-gromov-wasserstein
  Gabriel Peyré, Marco Cuturi, Justin Solomon, Gromov-Wasserstein Averaging of Kernel and Distance Matrices, Proc. of ICML 2016.
  ☆73Updated 8 years ago
• vivienseguy / Large-Scale-OT
  Stochastic algorithms for computing Regularized Optimal Transport
  ☆57Updated 6 years ago
• MichaelArbel / Scaled-MMD-GAN
  Scaled MMD GAN
  ☆36Updated 5 years ago
• YingzhenLi / Dropout_BBalpha
  Implementations of the ICML 2017 paper (with Yarin Gal)
  ☆38Updated 7 years ago
• fpcasale / GPPVAE
  Gaussian Process Prior Variational Autoencoder
  ☆84Updated 6 years ago
• eifuentes / swae-pytorch
  Implementation of the Sliced Wasserstein Autoencoder using PyTorch
  ☆103Updated 6 years ago
• gpeyre / 2017-MCOM-unbalanced-ot
  L. Chizat, G. Peyré, B. Schmitzer, F-X. Vialard. Scaling Algorithms for Unbalanced Transport Problems. Preprint Arxiv:1607.05816, 2016.
  ☆42Updated 8 years ago
• kekeblom / DeepCGP
  Deep convolutional gaussian processes.
  ☆79Updated 5 years ago
• pawni / BayesByHypernet
  Code for the paper Implicit Weight Uncertainty in Neural Networks
  ☆65Updated 5 years ago
• bfarzin / pytorch_aae
  Pytorch Adversarial Auto Encoder (AAE)
  ☆88Updated 6 years ago
• MinhyungCho / riemannian-batch-normalization-pytorch
  Riemannian approach to batch normalization
  ☆22Updated 7 years ago
• mauriziofilippone / deep_gp_random_features
  ☆40Updated 6 years ago
• audeg / StochasticOT
  Stochastic Optimization for Optimal Transport
  ☆22Updated 8 years ago
• mingzhang-yin / SIVI
  A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…
  ☆54Updated 9 months ago
• rflamary / JDOT
  Joint distribution optimal transportation for domain adaptation
  ☆102Updated 7 years ago
• zhenxuan00 / graphical-gan
  source code of the paper Graphical Generative Adversarial Networks
  ☆71Updated 6 years ago
• casperkaae / LVAE
  Code for "How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks"
  ☆101Updated 9 years ago
• geniki / neural-processes
  PyTorch implementation of Neural Processes
  ☆89Updated 6 years ago
• arolet / wasserstein-dictionary-learning
  A matlab toolbox to perform Wasserstein Dictionary Learning or NMF
  ☆32Updated 9 years ago
• ssydasheng / Neural-Kernel-Network
  Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326
  ☆71Updated 7 years ago
• microsoft / deterministic-variational-inference
  Sample code for running deterministic variational inference to train Bayesian neural networks
  ☆100Updated 6 years ago
• thangbui / deepGP_approxEP
  see https://github.com/thangbui/geepee for a faster implementation
  ☆37Updated 8 years ago
• RobRomijnders / bayes_nn
  Uncertainty interpretations of the neural network
  ☆32Updated 7 years ago
• LiamCattell / optimaltransport
  Optimal transport transforms
  ☆23Updated 7 years ago
• ermongroup / a-nice-mc
  Code for "A-NICE-MC: Adversarial Training for MCMC"
  ☆125Updated 6 years ago
• maka89 / Deep-Kernel-GP
  Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood
  ☆93Updated 7 years ago
• tvayer / SGW
  Code for Sliced Gromov-Wasserstein
  ☆68Updated 5 years ago
• ishansd / swg
  Sliced Wasserstein Generator
  ☆38Updated 7 years ago