Alternatives and similar repositories for TMD

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

• navid-naderi / WEGL
  The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).
  ☆35Updated 4 years ago
• FilippoMB / Total-variation-graph-neural-networks
  Pytorch (PyG) and Tensorflow (Keras/Spektral) implementation of Total Variation Graph Neural Network (TVGNN), as presented at ICML 2023.
  ☆20Updated 6 months ago
• samihaija / isvd
  Official implementation of NeurIPS'21: Implicit SVD for Graph Representation Learning
  ☆20Updated 3 years ago
• hadarser / SetToGraphPaper
  SetToGraph paper repository
  ☆22Updated 4 years ago
• dalab / matrix-manifolds
  Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper
  ☆36Updated 5 years ago
• SwiftieH / SpGAT
  Spectral Graph Attention Network with Fast Eigen-approximation
  ☆12Updated 3 years ago
• mfederici / dsit
  Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"
  ☆25Updated 3 years ago
• francoispierrepaty / SubspaceRobustWasserstein
  Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"
  ☆29Updated 6 years ago
• ferrine / hyrnn
  Hyperbolic Neural Networks, pytorch
  ☆87Updated 6 years ago
• oskopek / mvae
  Mixed-curvature Variational Autoencoders (ICLR 2020)
  ☆65Updated 4 years ago
• cscarv / riemannian-metric-learning-ot
  ☆17Updated 2 years ago
• CUAI / Differentiable-Frechet-Mean
  [ICML 2020] Differentiating through the Fréchet Mean (https://arxiv.org/abs/2003.00335).
  ☆58Updated 3 years ago
• ngruver / lie-deriv
  ☆38Updated 2 years ago
• MarcTLaw / UltrahyperbolicRepresentation
  Ultrahyperbolic Representation Learning
  ☆13Updated 5 years ago
• delta2323 / gnn-asymptotics
  Code for Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.
  ☆30Updated 5 years ago
• gasteigerjo / lcn
  Locally corrected Nyström (LCN), as proposed in "Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, …
  ☆19Updated 2 years ago
• Felix-Petersen / difftopk
  Differentiable Top-k Classification Learning
  ☆85Updated 2 years ago
• flyingtango / DiGCL
  The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021
  ☆36Updated 3 years ago
• danielegrattarola / GINR
  Code for "Generalised Implicit Neural Representations" (NeurIPS 2022).
  ☆71Updated 2 years ago
• bunnech / gwgan
  Learning Generative Models across Incomparable Spaces (ICML 2019)
  ☆27Updated 5 years ago
• SwiftieH / IGNN
  Implicit Graph Neural Networks
  ☆62Updated 3 years ago
• MichaelArbel / GeneralizedEBM
  ☆54Updated last year
• karl-zhao / benchmarking-gnns-pyg
  ☆31Updated 2 years ago
• trneedham / Spectral-Gromov-Wasserstein
  Graph matching and clustering by comparing heat kernels via optimal transport.
  ☆27Updated 2 years ago
• YannDubs / Invariant-Self-Supervised-Learning
  Pytorch code for "Improving Self-Supervised Learning by Characterizing Idealized Representations"
  ☆41Updated 2 years ago
• nkeriven / random-graph-gnn
  ☆12Updated 4 years ago
• cvignac / SMP
  ☆28Updated 3 years ago
• CharlotteMagister / GCExplainer
  ☆19Updated last year
• cambridge-mlg / CLUE
  Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"
  ☆36Updated last year
• facebookresearch / neural_stpp
  Deep generative modeling for time-stamped heterogeneous data, enabling high-fidelity models for a large variety of spatio-temporal domain…
  ☆104Updated 3 years ago