Alternatives and similar repositories for UoMH:

Users that are interested in UoMH 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
• bunnech / gwgan
  Learning Generative Models across Incomparable Spaces (ICML 2019)
  ☆27Updated 5 years ago
• chingyaoc / TMD
  NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks
  ☆36Updated last year
• thibsej / unbalanced_gromov_wasserstein
  Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport
  ☆44Updated 2 years ago
• meyerscetbon / LOT
  ☆17Updated 2 years ago
• mfederici / dsit
  Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"
  ☆25Updated 3 years ago
• AlCorreia / cm-tpm
  Code in support of the paper Continuous Mixtures of Tractable Probabilistic Models
  ☆12Updated 6 months ago
• ratschlab / SVGP-VAE
  Tensorflow implementation for the SVGP-VAE model.
  ☆22Updated 3 years ago
• aangelopoulos / private_prediction_sets
  Wrap around any model to output differentially private prediction sets with finite sample validity on any dataset.
  ☆17Updated last year
• cscarv / riemannian-metric-learning-ot
  ☆17Updated last year
• francoispierrepaty / SubspaceRobustWasserstein
  Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"
  ☆28Updated 5 years ago
• MarkPKCollier / MissingDataVAEs
  ☆19Updated 4 years ago
• dmilios / dirichletGPC
  ☆17Updated 6 years ago
• kevtimova / deep-sparse
  We introduce a way to extend sparse dictionary learning to deep architectures.
  ☆16Updated 3 years ago
• rpatrik96 / nl-causal-representations
  This is the code for the paper Jacobian-based Causal Discovery with Nonlinear ICA, demonstrating how identifiable representations (partic…
  ☆18Updated 8 months ago
• MarcTLaw / UltrahyperbolicRepresentation
  Ultrahyperbolic Representation Learning
  ☆12Updated 4 years ago
• locuslab / orthogonal-convolutions
  Implementations of orthogonal and semi-orthogonal convolutions in the Fourier domain with applications to adversarial robustness
  ☆44Updated 4 years ago
• lazaratan / dyn-gfn
  DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks
  ☆53Updated last year
• rpatrik96 / ima-vae
  This is the code for the paper Embrace the Gap: VAEs perform Independent Mechanism Analysis, showing that optimizing the ELBO is equivale…
  ☆23Updated last year
• iamalexkorotin / Wasserstein2Barycenters
  PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization" (ICLR 2021)
  ☆23Updated 2 years ago
• dalab / matrix-manifolds
  Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper
  ☆36Updated 4 years ago
• gwgundersen / rflvm
  Random feature latent variable models in Python
  ☆22Updated last year
• HongtengXu / Relational-AutoEncoders
  Learning Autoencoders with Relational Regularization
  ☆46Updated 4 years ago
• layer6ai-labs / two_step_zoo
  Codebase accompanying the paper "Diagnosing and Fixing Manifold Overfitting in Deep Generative Models"
  ☆9Updated 8 months ago
• BorgwardtLab / filtration_curves
  Code for the KDD 2021 paper 'Filtration Curves for Graph Representation'
  ☆18Updated last year
• vivienseguy / Large-Scale-OT
  Stochastic algorithms for computing Regularized Optimal Transport
  ☆57Updated 6 years ago
• csinva / mdl-complexity
  MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".
  ☆18Updated last year
• ermongroup / BCD-Nets
  Code for `BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery`, Neurips 2021
  ☆26Updated 3 years ago
• LuisArmandoPerez / DiffusionVAE
  ☆29Updated 3 years ago
• danielgreenfeld3 / XIC
  Robust Learning with the Hilbert-Schmidt Independence Criterion
  ☆46Updated 4 years ago