Harshs27 / neural-graphical-models
Neural Graphical models are neural network based graphical models that offer richer representation, faster inference & sampling
☆27Updated 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 code repository to the corresponding paper.☆28Updated last year
- Differentiable Euler Characteristic Transform☆17Updated 7 months ago
- Recursive Bayesian Estimation (Sequential / Online Inference)☆58Updated 9 months ago
- PyTorch implementation for "Probabilistic Circuits for Variational Inference in Discrete Graphical Models", NeurIPS 2020☆15Updated 3 years ago
- Investigate the speed of adaptation of structural causal models☆16Updated 3 years ago
- This is the code for the paper Jacobian-based Causal Discovery with Nonlinear ICA, demonstrating how identifiable representations (partic…☆16Updated 4 months ago
- Structured Neural Networks☆13Updated 7 months ago
- Automatic Integration for Neural Spatio-Temporal Point Process models (AI-STPP) is a new paradigm for exact, efficient, non-parametric inf…☆24Updated 3 months ago
- Composable kernels for scikit-learn implemented in JAX.☆42Updated 4 years ago
- Official repository for the paper "Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules" (…☆18Updated 2 years ago
- ☆41Updated 2 months ago
- Random feature latent variable models in Python☆22Updated last year
- Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding☆21Updated 2 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
- Official Implementation of the ICML 2023 paper: "Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally …☆69Updated last year
- Uncertainty in Conditional Average Treatment Effect Estimation☆29Updated 3 years ago
- Code for "Bayesian Structure Learning with Generative Flow Networks"☆82Updated 2 years ago
- ☆16Updated 3 years ago
- Quantification of Uncertainty with Adversarial Models☆27Updated last year
- Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen☆25Updated 4 years ago
- ☆15Updated 8 months ago
- Tangle Software Library☆29Updated 8 months ago
- Implementations of growing and pruning in neural networks☆22Updated last year
- A basic implementation of the paper Eigengame : PCA as a Nash Equilibrium☆21Updated 3 years ago
- Amortized Inference for Causal Structure Learning, NeurIPS 2022☆61Updated 2 weeks ago
- A minimal implementation of a VAE with BinConcrete (relaxed Bernoulli) latent distribution in TensorFlow.☆21Updated 4 years ago
- Pytorch implementation of VAEs for heterogeneous likelihoods.☆42Updated 2 years ago
- DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks☆51Updated 10 months ago
- Bayesian model reduction for probabilistic machine learning☆10Updated last month
- Official implementation of the paper "Interventions, Where and How? Experimental Design for Causal Models at Scale", NeurIPS 2022.☆19Updated 2 years ago