Alternatives and similar repositories for RTD

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

• danchern97 / RTD_AE
  This is an official repository for "Learning topology-preserving data representations" presented at ICLR 2023 conference.
  ☆35Updated 2 years ago
• milenagazdieva / U-NOTBarycenters
  PyTorch implementation of "Robust Barycenter Estimation using Semi-unbalanced Neural Optimal Transport" (ICLR 2025)
  ☆13Updated 5 months ago
• BorgwardtLab / topological-autoencoders
  Code for the paper "Topological Autoencoders" by Michael Moor, Max Horn, Bastian Rieck, and Karsten Borgwardt.
  ☆152Updated 3 years ago
• milenagazdieva / LightUnbalancedOptimalTransport
  PyTorch implementation of "Light Unbalanced Optimal Transport" (NeurIPS 2024)
  ☆19Updated 11 months ago
• IlyaTrofimov / MTopDiv
  [NeurIPS 2021] Manifold Topology Divergence: a Framework for Comparing Data Manifolds
  ☆15Updated 3 years ago
• ppope / dimensions
  Code for "The Intrinsic Dimension of Images and Its Impact on Learning" - ICLR 2021 Spotlight https://openreview.net/forum?id=XJk19XzGq2J
  ☆71Updated last year
• tlacombe / topologicalUncertainty
  ☆11Updated 3 years ago
• dimkal89 / rvae
  ☆37Updated 5 years ago
• microsoft / otdd
  Optimal Transport Dataset Distance
  ☆173Updated 3 years ago
• thibsej / unbalanced_gromov_wasserstein
  Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport
  ☆45Updated 2 years ago
• cscarv / riemannian-metric-learning-ot
  ☆18Updated 2 years ago
• justkolesov / Wasserstein1Benchmark
  A set of tests for evaluating large-scale algorithms for Wasserstein-1 transport computation (NeurIPS'22).
  ☆21Updated last year
• PetrMokrov / Large-Scale-Wasserstein-Gradient-Flows
  Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)
  ☆39Updated 3 years ago
• georgiosarvanitidis / geometric_ml
  This repository contains code for applying Riemannian geometry in machine learning.
  ☆78Updated 4 years ago
• c-hofer / torchph
  The essence of my research, distilled for reusability. Enjoy 🥃!
  ☆71Updated last year
• ansuini / IntrinsicDimDeep
  Code for the intrinsic dimensionality estimate of data representations
  ☆86Updated 5 years ago
• navid-naderi / WEGL
  The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).
  ☆35Updated 4 years ago
• tolgabirdal / PHDimGeneralization
  Official implementation of "Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks", NeurIPS 2021.
  ☆32Updated 8 months ago
• sharpenb / Posterior-Network
  Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)
  ☆78Updated 3 years ago
• federicobergamin / riemannian-laplace-approximation
  Repository for the paper "Riemannian Laplace approximations for Bayesian neural networks"
  ☆11Updated last year
• YannDubs / Invariant-Self-Supervised-Learning
  Pytorch code for "Improving Self-Supervised Learning by Characterizing Idealized Representations"
  ☆41Updated 3 years ago
• tvayer / FGW
  Code for Optimal Transport for structured data with application on graphs
  ☆103Updated 2 years ago
• AndyShih12 / mac
  PyTorch implementation for "Training and Inference on Any-Order Autoregressive Models the Right Way", NeurIPS 2022 Oral, TPM 2023 Best Pa…
  ☆15Updated 2 years ago
• ngushchin / EntropicNeuralOptimalTransport
  Pytorch implementation of "Entropic Neural Optimal Transport via Diffusion Processes" (NeurIPS 2023, oral).
  ☆39Updated last year
• iamalexkorotin / Wasserstein2Barycenters
  PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization" (ICLR 2021)
  ☆25Updated 3 years ago
• bunnech / jkonet
  Proximal Optimal Transport Modeling of Population Dynamics (AISTATS 2022)
  ☆22Updated 2 years ago
• zengyi-li / NMCE-release
  Code for Neural Manifold Clustering and Embedding
  ☆61Updated 3 years ago
• iamalexkorotin / Wasserstein2Benchmark
  A set of tests for evaluating large-scale algorithms for Wasserstein-2 transport maps computation (NeurIPS 2021)
  ☆43Updated 3 years ago
• runame / laplace-refinement
  Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
  ☆10Updated 3 years ago
• trneedham / gromov-wasserstein-statistics
  Statistics on the space of asymmetric networks via Gromov-Wasserstein distance
  ☆15Updated 5 years ago