Harshs27 / neural-graphical-modelsLinks
Neural Graphical models are neural network based graphical models that offer richer representation, faster inference & sampling
☆29Updated 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
Sorting:
- ☆192Updated 3 weeks ago
- Implementation of the unbounded depth neural network from the paper Variational Inference for Infinitely Deep Neural Networks☆17Updated 2 years ago
- Composable kernels for scikit-learn implemented in JAX.☆43Updated 4 years ago
- This repository contains a Jax implementation of conformal training corresponding to the ICLR'22 paper "learning optimal conformal classi…☆130Updated 2 years ago
- Recursive Bayesian Estimation (Sequential / Online Inference)☆59Updated last year
- Official Implementation of the ICML 2023 paper: "Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally …☆72Updated 2 years ago
- Code for: "Neural Rough Differential Equations for Long Time Series", (ICML 2021)☆118Updated 4 years ago
- Official implementation of the paper "Interventions, Where and How? Experimental Design for Causal Models at Scale", NeurIPS 2022.☆20Updated 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 9 months ago
- Official implementation of E(n)-equivariant Graph Neural Cellular Automata☆29Updated last year
- Random feature latent variable models in Python☆22Updated last year
- The Energy Transformer block, in JAX☆57Updated last year
- Official repository for the paper "Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules" (…☆23Updated last month
- Official code repository to the corresponding paper.☆29Updated last year
- This is the code for the paper Jacobian-based Causal Discovery with Nonlinear ICA, demonstrating how identifiable representations (partic…☆18Updated 10 months ago
- Neat Bayesian machine learning examples☆58Updated 3 weeks ago
- Official repository for the paper "Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks"☆59Updated 3 years ago
- Code for "Bayesian Structure Learning with Generative Flow Networks"☆87Updated 3 years ago
- codebase for "A Theory of the Inductive Bias and Generalization of Kernel Regression and Wide Neural Networks"☆49Updated 2 years ago
- Variational inference for hierarchical dynamical systems☆48Updated 11 months ago
- Materials of the Nordic Probabilistic AI School 2023.☆90Updated last year
- Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen☆25Updated 5 years ago
- Tensorflow implementation and notebooks for Implicit Maximum Likelihood Estimation☆67Updated 3 years ago
- Simplicial neural networks (SNNs), a generalization of graph neural networks to data that live on a class of topological spaces called si…☆80Updated 4 years ago
- Official code for UnICORNN (ICML 2021)☆27Updated 3 years ago
- Public Implementation of Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes☆49Updated 2 years ago
- Official codebase for "Distribution-Free, Risk-Controlling Prediction Sets"☆85Updated last year
- Bayesian model reduction for probabilistic machine learning☆11Updated 2 weeks ago
- Quantification of Uncertainty with Adversarial Models☆30Updated 2 years ago
- Meta-learning inductive biases in the form of useful conserved quantities.☆37Updated 2 years ago