Alternatives and similar repositories for StructuredNNs:

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

• larslorch / avici
  Amortized Inference for Causal Structure Learning, NeurIPS 2022
  ☆63Updated 2 months ago
• tristandeleu / jax-dag-gflownet
  Code for "Bayesian Structure Learning with Generative Flow Networks"
  ☆87Updated 3 years ago
• psanch21 / causal-flows
  ☆52Updated 9 months ago
• microsoft / csuite
  CSuite: A Suite of Benchmark Datasets for Causality
  ☆67Updated last year
• phlippe / ENCO
  Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"
  ☆82Updated last year
• runame / laplace-redux
  Laplace Redux -- Effortless Bayesian Deep Learning
  ☆43Updated 2 years ago
• jongharyu / neural-svd
  ☆16Updated 7 months ago
• Harshs27 / neural-graphical-models
  Neural Graphical models are neural network based graphical models that offer richer representation, faster inference & sampling
  ☆28Updated last year
• LaurenceA / bayesfunc
  ☆15Updated 2 years ago
• ssokota / mec
  Code for minimum-entropy coupling.
  ☆31Updated 9 months ago
• piomonti / carefl
  Code for "Causal autoregressive flows" - AISTATS, 2021
  ☆44Updated 4 years ago
• EdinburghNLP / torch-adaptive-imle
  ☆34Updated 4 months ago
• GFNOrg / gfn-diffusion
  ☆27Updated 3 weeks ago
• michaelsdr / sinkformers
  Transformers with doubly stochastic attention
  ☆45Updated 2 years ago
• yannadani / cbed
  Official implementation of the paper "Interventions, Where and How? Experimental Design for Causal Models at Scale", NeurIPS 2022.
  ☆20Updated 2 years ago
• leonard-gleyzer / connex
  Fine-grained, dynamic control of neural network topology in JAX.
  ☆21Updated last year
• lazaratan / dyn-gfn
  DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks
  ☆52Updated last year
• tychovdo / lila
  Code for "Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations"
  ☆23Updated 2 years ago
• bradyneal / realcause
  Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed caus…
  ☆74Updated 4 years ago
• probabilisticai / probai-2023
  Materials of the Nordic Probabilistic AI School 2023.
  ☆91Updated last year
• phlippe / CITRIS
  Code repository of the paper "CITRIS: Causal Identifiability from Temporal Intervened Sequences" and "iCITRIS: Causal Representation Lear…
  ☆51Updated last year
• akekic / causal-component-analysis
  ☆25Updated last year
• google-deepmind / annealed_flow_transport
  ☆52Updated 2 years ago
• acerbilab / amortized-conditioning-engine
  Amortized Probabilistic Conditioning for Optimization, Simulation and Inference (Chang et al., AISTATS 2025)
  ☆14Updated this week
• requeima / llm_processes
  ☆58Updated 3 weeks ago
• gebob19 / introduction_to_normalizing_flows
  Jupyter Notebook corresponding to 'Going with the Flow: An Introduction to Normalizing Flows'
  ☆26Updated 4 years ago
• adrianjav / heterogeneous_vaes
  Pytorch implementation of VAEs for heterogeneous likelihoods.
  ☆42Updated 2 years ago
• saleml / continuous-gfn
  ☆24Updated last year
• PrincetonLIPS / AHGP
  [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)
  ☆21Updated 4 years ago
• AWehenkel / Graphical-Normalizing-Flows
  Combining smooth constraint for building DAG with normalizing flow in order to replace autoregressive transformations while keeping tract…
  ☆45Updated last year