Alternatives and similar repositories for nodags-flows

Users that are interested in nodags-flows are comparing it to the libraries listed below

• azizilab / sdcd
  Stable Differentiable Causal Discovery (SDCD)
  ☆25Updated last year
• ratschlab / SVGP-VAE
  Tensorflow implementation for the SVGP-VAE model.
  ☆22Updated 4 years ago
• lazaratan / dyn-gfn
  DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks
  ☆59Updated 4 months ago
• uhlerlab / causaldag
  Python package for the creation, manipulation, and learning of Causal DAGs
  ☆156Updated 2 years ago
• causalbench / causalbench
  ☆57Updated 7 months ago
• Genentech / dcdfg
  ☆28Updated 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…
  ☆22Updated last year
• bunnech / condot
  Supervised Training of Conditional Monge Maps
  ☆19Updated 2 years ago
• slachapelle / dcdi
  Repository for "Differentiable Causal Discovery from Interventional Data"
  ☆77Updated 3 years ago
• KrishnaswamyLab / MIOFlow
  ☆34Updated 2 months ago
• bunnech / jkonet
  Proximal Optimal Transport Modeling of Population Dynamics (AISTATS 2022)
  ☆22Updated 2 years ago
• KrishnaswamyLab / DiffusionEMD
  Code corresponding to the paper Diffusion Earth Mover's Distance and Distribution Embeddings
  ☆38Updated last year
• RECOVERcoalition / Recover
  Exploring the space of drug combinations to discover synergistic drugs using Active Learning
  ☆24Updated last year
• i6092467 / vadesc
  A probabilistic model to cluster survival data in a variational deep clustering setting
  ☆30Updated 3 years ago
• atong01 / MultiscaleEMD
  Multiscale earth mover's distance methods for tree and manifold structured data
  ☆12Updated 10 months ago
• PythonOT / COOT
  CO-Optimal Transport
  ☆42Updated 5 years ago
• uhlerlab / actlearn_optint
  Active learning for optimal intervention design in causal models
  ☆16Updated 2 years ago
• navid-naderi / WEGL
  The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).
  ☆35Updated 4 years ago
• ahmedmalaa / evaluating-generative-models
  ☆22Updated 2 years ago
• Genentech / MMFM
  ☆18Updated last year
• lmotte / graph-prediction-with-fused-gromov-wasserstein
  Python implementation of the supervised graph prediction method proposed in http://arxiv.org/abs/2202.03813 using PyTorch library and POT…
  ☆14Updated 3 years ago
• clarafy / conformal-for-design
  ☆28Updated 3 years ago
• vios-s / DiffAN
  Diffusion Models for Causal Discovery
  ☆90Updated 2 years ago
• thibsej / unbalanced_gromov_wasserstein
  Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport
  ☆45Updated 2 years ago
• psanch21 / causal-flows
  ☆52Updated last year
• alexisbellot / Graphical-modelling-continuous-time
  Graphical modelling with time series data using an ODE model
  ☆12Updated 3 years ago
• rflperry / sparse_shift
  ☆11Updated 3 years ago
• gemoran / sparse-vae-code
  ☆31Updated 3 years ago
• yulun-rayn / variational-causal-inference
  Counterfactual Generative Modeling with Variational Causal Inference (ICLR 2025)
  ☆20Updated 3 months ago
• Genentech / sVAE
  ☆64Updated 2 years ago