Alternatives and similar repositories for Multilevel-Wasserstein-Means

Users that are interested in Multilevel-Wasserstein-Means are comparing it to the libraries listed below

• icemiliang / pyvot
  A Python implementation of Monge optimal transportation
  ☆49Updated 2 years ago
• francoispierrepaty / SubspaceRobustWasserstein
  Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"
  ☆29Updated 6 years ago
• hichamjanati / mtw
  Wasserstein regularization for sparse multi-task regression
  ☆15Updated 5 years ago
• mikigom / large-scale-OT-mapping-TF
  Tensorflow Implementation of "Large-scale Optimal Transport and Mapping Estimation"(ICLR2018/NIPS 2017 OTML)
  ☆20Updated 7 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.
  ☆74Updated 9 years ago
• mducoffe / Learning-Wasserstein-Embeddings
  Keras implementation of Deep Wasserstein Embeddings
  ☆48Updated 7 years ago
• tvayer / SGW
  Code for Sliced Gromov-Wasserstein
  ☆69Updated 5 years ago
• jeanfeydy / global-divergences
  MMD, Hausdorff and Sinkhorn divergences scaled up to 1,000,000 samples.
  ☆56Updated 6 years ago
• matthieuheitz / WassersteinDictionaryLearning
  Morgan A. Schmitz., Matthieu Heitz, Nicolas Bonneel, Fred Ngole, David Coeurjolly, Marco Cuturi, Gabriel Peyré, and Jean-Luc Starck. "Was…
  ☆20Updated 5 years ago
• mrojascarulla / causal_transfer_learning
  ☆32Updated 7 years ago
• rflamary / OTML_DS3_2018
  Practical sessions for the Optimal Transport and Machine learning course at DS3 2018
  ☆92Updated 7 years ago
• wittawatj / interpretable-test
  NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.
  ☆64Updated 7 years ago
• skolouri / swae
  Implementation of the Sliced Wasserstein Autoencoders
  ☆90Updated 7 years ago
• skolouri / swgmm
  Sliced Wasserstein Distance for Learning Gaussian Mixture Models
  ☆66Updated 2 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
• didriknielsen / ds3_abi_exercises
  Exercises for the Tutorial on Approximate Bayesian Inference at the Data Science Summer School 2018
  ☆22Updated 7 years ago
• davidhallac / NetworkLasso
  ☆22Updated 10 years ago
• namkoong-lab / robustopt
  ☆43Updated 7 years ago
• kekeblom / DeepCGP
  Deep convolutional gaussian processes.
  ☆80Updated 6 years ago
• blei-lab / ars-reparameterization
  Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)
  ☆38Updated 8 years ago
• vivienseguy / Large-Scale-OT
  Stochastic algorithms for computing Regularized Optimal Transport
  ☆58Updated 7 years ago
• talwagner / efficient_kde
  Efficient LSH-based kernel density estimation
  ☆29Updated 5 years ago
• mblondel / smooth-ot
  Python implementation of smooth optimal transport.
  ☆60Updated 4 years ago
• pawni / BayesByHypernet
  Code for the paper Implicit Weight Uncertainty in Neural Networks
  ☆65Updated 5 years ago
• google / wasserstein-dist
  tensorflow implementation of the Wasserstein (aka optimal transport) distance
  ☆72Updated 4 years ago
• IBM / wasserstein-barycenters
  Code for our ICLR19 paper "Wasserstein Barycenters for Model Ensembling", Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jarret Ross, Cicero…
  ☆22Updated 5 years ago
• jiayuzhou / MALSAR
  Multi-task learning via Structural Regularization
  ☆134Updated 4 years ago
• icml-mcbn / mcbn
  ☆53Updated 7 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 11 months ago
• brain-research / ncp
  Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors
  ☆62Updated 5 years ago