Harshs27 / neural-graphical-models
Neural Graphical models are neural network based graphical models that offer richer representation, faster inference & sampling
☆28Updated last year
Alternatives and similar repositories for neural-graphical-models:
Users that are interested in neural-graphical-models are comparing it to the libraries listed below
- Official implementation of the paper "Interventions, Where and How? Experimental Design for Causal Models at Scale", NeurIPS 2022.☆20Updated 2 years ago
- Code for "Bayesian Structure Learning with Generative Flow Networks"☆87Updated 3 years ago
- Official code repository to the corresponding paper.☆29Updated last year
- Structured Neural Networks☆14Updated 11 months ago
- Utilities for probabilistic ML☆34Updated last year
- Bayesian Bandits☆68Updated last year
- Recursive Bayesian Estimation (Sequential / Online Inference)☆58Updated last year
- Pytorch implementation of VAEs for heterogeneous likelihoods.☆42Updated 2 years ago
- This is the code for the paper Jacobian-based Causal Discovery with Nonlinear ICA, demonstrating how identifiable representations (partic…☆18Updated 7 months ago
- Bayesian model reduction for probabilistic machine learning☆11Updated last week
- Treeffuser is an easy-to-use package for probabilistic prediction and probabilistic regression on tabular data with tree-based diffusion …☆43Updated 2 months ago
- Neat Bayesian machine learning examples☆55Updated 3 months ago
- ☆51Updated 8 months ago
- stand alone Neural Additive Models, forked from google-reasearch for easy import to colab☆28Updated 4 years ago
- Official Implementation of the ICML 2023 paper: "Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally …☆70Updated last year
- Composable kernels for scikit-learn implemented in JAX.☆43Updated 4 years ago
- Amortized Inference for Causal Structure Learning, NeurIPS 2022☆62Updated 2 months ago
- Implementation of the unbounded depth neural network from the paper Variational Inference for Infinitely Deep Neural Networks☆17Updated 2 years ago
- GRAPL: A computational library for nonparametric structural causal modelling, analysis and inference☆81Updated 4 months ago
- Neural Tangent Kernel (NTK) module for the scikit-learn library☆25Updated 6 months ago
- PyTorch implementation for "Probabilistic Circuits for Variational Inference in Discrete Graphical Models", NeurIPS 2020☆17Updated 3 years ago
- Eastern European Machine Learning Summer School (EEML) Workshop Series 2022. Tutorial on Causality for the Serbian Machine Learning Works…☆21Updated 2 years ago
- python app for doing personalized causal medicine using the methods invented by Judea Pearl et al.☆25Updated 2 years ago
- Automatic Integration for Neural Spatio-Temporal Point Process models (AI-STPP) is a new paradigm for exact, efficient, non-parametric inf…☆24Updated 6 months ago
- Official repository for the paper "Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules" (…☆22Updated 2 years ago
- ☆11Updated 2 months ago
- Tensorflow implementation and notebooks for Implicit Maximum Likelihood Estimation☆67Updated 3 years ago
- Repo to accompany paper "Implicit Self-Regularization in Deep Neural Networks..."☆43Updated 6 years ago
- DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks☆52Updated last year
- Jax SSM Library☆49Updated 2 years ago