Alternatives and similar repositories for hyplinear

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

• CUAI / Differentiable-Frechet-Mean
  [ICML 2020] Differentiating through the Fréchet Mean (https://arxiv.org/abs/2003.00335).
  ☆56Updated 3 years ago
• mblondel / smooth-ot
  Python implementation of smooth optimal transport.
  ☆58Updated 4 years ago
• gabsens / Learning-Embeddings-into-Entropic-Wasserstein-Spaces-ENSAE
  A thorough review of the paper "Learning Embeddings into Entropic Wasserstein Spaces" by Frogner et al. Includes a reproduction of the re…
  ☆22Updated 5 years ago
• dalab / matrix-manifolds
  Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper
  ☆36Updated 5 years ago
• ydtydr / HyperbolicTiling_Learning
  Official implementation (learning part) for paper: Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models
  ☆18Updated 11 months ago
• sidak / context-mover-distance-and-barycenters
  Code accompanying our paper at AISTATS 2020
  ☆21Updated 4 years ago
• bluer555 / KernelGCN
  Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs
  ☆34Updated 5 years ago
• Cyanogenoid / fspool
  [ICLR 2020] FSPool: Learning Set Representations with Featurewise Sort Pooling
  ☆42Updated last year
• PurdueMINDS / Equivalence
  Equivalence Between Structural Representations and Positional Node Embeddings
  ☆22Updated 5 years ago
• oskopek / mvae
  Mixed-curvature Variational Autoencoders (ICLR 2020)
  ☆62Updated 4 years ago
• HongtengXu / Relational-Factorization-Model
  Gromov-Wasserstein Factorization Models for Graph Clustering (AAAI-20)
  ☆31Updated 2 years ago
• ferrine / hyrnn
  Hyperbolic Neural Networks, pytorch
  ☆86Updated 5 years ago
• chingyaoc / TMD
  NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks
  ☆37Updated last year
• HongtengXu / gwl
  Gromov-Wasserstein Learning for Graph Matching and Node Embedding
  ☆72Updated 5 years ago
• mducoffe / Learning-Wasserstein-Embeddings
  Keras implementation of Deep Wasserstein Embeddings
  ☆48Updated 7 years ago
• arthurmensch / online_sinkhorn
  ☆13Updated 4 years ago
• deep-spin / lp-sparsemap
  LP-SparseMAP: Differentiable sparse structured prediction in coarse factor graphs
  ☆41Updated last year
• giannisnik / repset
  Rep the Set: Neural Networks for Learning Set Representations
  ☆29Updated 4 years ago
• hichamjanati / mtw
  Wasserstein regularization for sparse multi-task regression
  ☆15Updated 4 years ago
• ec6dde01667145e58de60f864e05a4 / CausalOptimizationAnon
  ☆65Updated 11 months ago
• jeanfeydy / global-divergences
  MMD, Hausdorff and Sinkhorn divergences scaled up to 1,000,000 samples.
  ☆56Updated 6 years ago
• dhruvdcoder / HyperA
  Started as a Team Project for CS690D at UMass Amherst, now turning into pytorch implementation of hyperbolic neural networks using Poinca…
  ☆12Updated 2 years ago
• matenure / OTCoarsening
  Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
  ☆37Updated 4 years ago
• NNReasoning / What-Can-Neural-Networks-Reason-About
  ☆44Updated 4 years ago
• kingofspace0wzz / wae-rnf-lm
  Pytorch Implemetation for our NAACL2019 Paper "Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling" http…
  ☆62Updated 5 years ago
• hichamjanati / Entropic-OT-gaussians
  Closed form Entropic OT for balanced and unbalanced Gaussians
  ☆17Updated 3 years ago
• ermongroup / subsets
  Code for Reparameterizable Subset Sampling via Continuous Relaxations, IJCAI 2019.
  ☆57Updated last year
• nkeriven / random-graph-gnn
  ☆12Updated 4 years ago
• IBM / HOTT
  Code for NeurIPS 2019 paper "Hierarchical Optimal Transport for Document Representation"
  ☆54Updated 5 years ago
• cshjin / gnn_in_neurips_2019
  A comprehensive collection of GNN works in NeurIPS 2019.
  ☆21Updated 5 years ago