Alternatives and similar repositories for sinkhorn_knopp

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

• vivienseguy / Large-Scale-OT
  Stochastic algorithms for computing Regularized Optimal Transport
  ☆58Updated 7 years ago
• tvayer / SGW
  Code for Sliced Gromov-Wasserstein
  ☆69Updated 6 years ago
• HeddaCohenIndelman / Learning-Gumbel-Sinkhorn-Permutations-w-Pytorch
  LEARNING LATENT PERMUTATIONS WITH GUMBEL-SINKHORN NETWORKS IMPLEMENTATION WITH PYTORCH
  ☆81Updated 2 years ago
• eifuentes / swae-pytorch
  Implementation of the Sliced Wasserstein Autoencoder using PyTorch
  ☆101Updated 7 years ago
• cambridge-mlg / DUN
  Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
  ☆78Updated 2 years ago
• CUAI / Differentiable-Frechet-Mean
  [ICML 2020] Differentiating through the Fréchet Mean (https://arxiv.org/abs/2003.00335).
  ☆59Updated 4 years ago
• skolouri / swgmm
  Sliced Wasserstein Distance for Learning Gaussian Mixture Models
  ☆66Updated 2 years ago
• claying / OTK
  A Pytorch implementation of the optimal transport kernel embedding
  ☆118Updated 4 years ago
• yassersouri / pytorch-deep-sets
  PyTorch re-implementation of parts of "Deep Sets" (NIPS 2017)
  ☆73Updated 7 years ago
• rythei / PyTorchOT
  implements optimal transport algorithms in pytorch
  ☆102Updated 3 years ago
• thibsej / unbalanced_gromov_wasserstein
  Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport
  ☆45Updated 2 years ago
• Alfo5123 / Variational-Sparse-Coding
  Reproducing the paper "Variational Sparse Coding" for the ICLR 2019 Reproducibility Challenge
  ☆62Updated 2 years ago
• brain-research / ncp
  Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors
  ☆62Updated 5 years ago
• mblondel / smooth-ot
  Python implementation of smooth optimal transport.
  ☆61Updated 4 years ago
• MichaelArbel / GeneralizedEBM
  ☆54Updated last year
• HongtengXu / Relational-AutoEncoders
  Learning Autoencoders with Relational Regularization
  ☆46Updated 5 years ago
• microsoft / deterministic-variational-inference
  Sample code for running deterministic variational inference to train Bayesian neural networks
  ☆101Updated 7 years ago
• sharpenb / Posterior-Network
  Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)
  ☆78Updated 3 years ago
• PolinaKirichenko / flows_ood
  ☆89Updated 4 years ago
• skolouri / swae
  Implementation of the Sliced Wasserstein Autoencoders
  ☆91Updated 7 years ago
• kamenbliznashki / generative_models
  Pytorch implementations of generative models: VQVAE2, AIR, DRAW, InfoGAN, DCGAN, SSVAE
  ☆93Updated 4 years ago
• asteroidhouse / self-tuning-networks
  Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088
  ☆58Updated 6 years ago
• alexalemi / vib_demo
  ☆93Updated 3 years ago
• team-approx-bayes / dnn2gp
  Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)
  ☆48Updated 6 years ago
• fpcasale / GPPVAE
  Gaussian Process Prior Variational Autoencoder
  ☆87Updated 7 years ago
• ritheshkumar95 / energy_based_generative_models
  PyTorch code accompanying our paper on Maximum Entropy Generators for Energy-Based Models
  ☆154Updated 6 years ago
• oskopek / mvae
  Mixed-curvature Variational Autoencoders (ICLR 2020)
  ☆65Updated 4 years ago
• ansuini / IntrinsicDimDeep
  Code for the intrinsic dimensionality estimate of data representations
  ☆86Updated 5 years ago
• modestyachts / neural_kernels_code
  ☆36Updated 4 years ago
• g-benton / learning-invariances
  Codebase for Learning Invariances in Neural Networks
  ☆96Updated 3 years ago