Alternatives and similar repositories for neural-graphical-models

Users that are interested in neural-graphical-models are comparing it to the libraries listed below

• akandykeller / NeuralWaveMachines
  Official Implementation of the ICML 2023 paper: "Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally …
  ☆76Updated 2 years ago
• google-deepmind / neural_testbed
  ☆192Updated 4 months ago
• probabilisticai / probai-2023
  Materials of the Nordic Probabilistic AI School 2023.
  ☆91Updated 2 years ago
• google-deepmind / conformal_training
  This repository contains a Jax implementation of conformal training corresponding to the ICLR'22 paper "learning optimal conformal classi…
  ☆129Updated 3 years ago
• probabilisticai / probai-2022
  Materials of the Nordic Probabilistic AI School 2022.
  ☆180Updated 3 years ago
• aangelopoulos / rcps
  Official codebase for "Distribution-Free, Risk-Controlling Prediction Sets"
  ☆87Updated last year
• jambo6 / neuralRDEs
  Code for: "Neural Rough Differential Equations for Long Time Series", (ICML 2021)
  ☆120Updated 4 years ago
• bhoov / hamux
  Hierarchical Associative Memory User Experience
  ☆104Updated last week
• aangelopoulos / prediction-powered-inference
  A statistical toolkit for scientific discovery using machine learning
  ☆80Updated last year
• probml / rebayes
  Recursive Bayesian Estimation (Sequential / Online Inference)
  ☆60Updated last year
• gugarosa / learnergy
  💡 Learnergy is a Python library for energy-based machine learning models.
  ☆69Updated 8 months ago
• gerdm / bayes
  Neat Bayesian machine learning examples
  ☆58Updated 2 weeks ago
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆75Updated 3 years ago
• adrianjav / heterogeneous_vaes
  Pytorch implementation of VAEs for heterogeneous likelihoods.
  ☆43Updated 3 years ago
• tristandeleu / jax-dag-gflownet
  Code for "Bayesian Structure Learning with Generative Flow Networks"
  ☆93Updated 3 years ago
• microsoft / vi-hds
  Variational inference for hierarchical dynamical systems
  ☆48Updated last year
• TorchUQ / torchuq
  A library for uncertainty quantification based on PyTorch
  ☆122Updated 3 years ago
• stefaniaebli / simplicial_neural_networks
  Simplicial neural networks (SNNs), a generalization of graph neural networks to data that live on a class of topological spaces called si…
  ☆81Updated 4 years ago
• BasisResearch / chirho
  An experimental language for causal reasoning
  ☆237Updated last month
• ExpectationMax / sklearn-jax-kernels
  Composable kernels for scikit-learn implemented in JAX.
  ☆45Updated 5 years ago
• probml / JSL
  Jax SSM Library
  ☆48Updated 2 years ago
• SamsungSAILMontreal / ForestDiffusion
  Generating and Imputing Tabular Data via Diffusion and Flow XGBoost Models
  ☆174Updated last year
• AmrMKayid / nam
  Neural Additive Models (Google Research)
  ☆73Updated 4 years ago
• ahmedmalaa / Symbolic-Metamodeling
  Codebase for "Demystifying Black-box Models with Symbolic Metamodels", NeurIPS 2019.
  ☆52Updated 6 years ago
• muandet-lab / ipml-course
  A course on imprecise probabilistic machine learning
  ☆105Updated this week
• ml-jku / quam
  Quantification of Uncertainty with Adversarial Models
  ☆29Updated 2 years ago
• larslorch / avici
  Amortized Inference for Causal Structure Learning, NeurIPS 2022
  ☆68Updated 9 months ago
• aangelopoulos / conformal-risk
  Conformal prediction for controlling monotonic risk functions. Simple accompanying PyTorch code for conformal risk control in computer vi…
  ☆72Updated 2 years ago
• KindXiaoming / BIMT
  Brain-Inspired Modular Training (BIMT), a method for making neural networks more modular and interpretable.
  ☆173Updated 2 years ago
• tgcsaba / GPSig
  Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
  ☆49Updated 2 years ago