Alternatives and similar repositories for topologicalUncertainty

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

• Pseudomanifold / pyper
  Python-based persistent homology algorithms
  ☆19Updated 2 years ago
• iamalexkorotin / Wasserstein2Barycenters
  PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization" (ICLR 2021)
  ☆33Updated 3 years ago
• federicobergamin / riemannian-laplace-approximation
  Repository for the paper "Riemannian Laplace approximations for Bayesian neural networks"
  ☆12Updated 2 years ago
• IlyaTrofimov / MTopDiv
  [NeurIPS 2021] Manifold Topology Divergence: a Framework for Comparing Data Manifolds
  ☆15Updated 3 years ago
• ximenafernandez / intrinsicPH
  This is the code related to the article 'Intrinsic persistent homology via density-based metric learning'
  ☆10Updated 2 years ago
• PythonOT / COOT
  CO-Optimal Transport
  ☆42Updated 5 years ago
• navid-naderi / WEGL
  The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).
  ☆35Updated 5 years ago
• c-hofer / torchph
  The essence of my research, distilled for reusability. Enjoy 🥃!
  ☆73Updated last year
• cscarv / riemannian-metric-learning-ot
  ☆18Updated 2 years ago
• cambridge-mlg / CLUE
  Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"
  ☆35Updated last year
• chingyaoc / TMD
  NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks
  ☆37Updated 2 years ago
• BorgwardtLab / topological-autoencoders
  Code for the paper "Topological Autoencoders" by Michael Moor, Max Horn, Bastian Rieck, and Karsten Borgwardt.
  ☆153Updated 4 years ago
• thibsej / unbalanced_gromov_wasserstein
  Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport
  ☆45Updated 2 years ago
• Genentech / nodags-flows
  ☆11Updated 5 months ago
• BorgwardtLab / ggme
  Official repository for the ICLR 2022 paper "Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions"…
  ☆14Updated 3 years ago
• azlawson / PersistenceCurves
  A python package for computing Persistence Curves
  ☆19Updated 4 years ago
• haguettaz / ChebLieNet
  ChebLieNet, a spectral graph neural network turned equivariant by Riemannian geometry on Lie groups.
  ☆14Updated last year
• bunnech / jkonet
  Proximal Optimal Transport Modeling of Population Dynamics (AISTATS 2022)
  ☆22Updated 2 years ago
• danchern97 / RTD_AE
  This is an official repository for "Learning topology-preserving data representations" presented at ICLR 2023 conference.
  ☆36Updated 3 years ago
• BorgwardtLab / filtration_curves
  Code for the KDD 2021 paper 'Filtration Curves for Graph Representation'
  ☆18Updated 2 years ago
• ggoh29 / Simplicial-neural-network-benchmark
  Simplicial neural network benchmarking software
  ☆17Updated 3 years ago
• ngruver / lie-deriv
  ☆39Updated 3 years ago
• runame / laplace-refinement
  Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
  ☆10Updated 3 years ago
• francoispierrepaty / SubspaceRobustWasserstein
  Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"
  ☆29Updated 6 years ago
• IlyaTrofimov / RTD
  [ICML 2022] Representation Topology Divergence: A Method for Comparing Neural Network Representations
  ☆25Updated 3 years ago
• ratschlab / SVGP-VAE
  Tensorflow implementation for the SVGP-VAE model.
  ☆22Updated 4 years ago
• tijana-zrnic / active-inference
  ☆18Updated last year
• ahmedmalaa / evaluating-generative-models
  ☆22Updated 2 years ago
• CSDUlm / wsingular
  Python package for the ICML 2022 paper "Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors".
  ☆10Updated last year
• runame / laplace-redux
  Laplace Redux -- Effortless Bayesian Deep Learning
  ☆44Updated 8 months ago