Doing Bayesian Data Analysis, 2nd Edition (Kruschke, 2015): Python/PyMC3 code

TAPAS - Translational Algorithms for Psychiatry-Advancing Science

Train and visualize Hierarchical Attention Networks

The base NIMBLE package for R

Clustering methods in Machine Learning includes both theory and python code of each algorithm. Algorithms include K Mean, K Mode, Hierarchical, DB Scan and Gaussian Mixture Model GMM. Interview questions on clustering are also added in the end.

Personal project to compare hierarchical linear regression in PyMC3 and PyStan, as presented at http://pydata.org/london2016/schedule/presentation/30/ video: https://www.youtube.com/watch?v=Jb9eklfbDyg

[IJCAI 2019] Source code and datasets for "Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification"

DrugHIVE: Structure-based drug design with a deep hierarchical generative model

PyTorch Implementation of Deep Hierarchical Classification for Category Prediction in E-commerce System

This is project page for the paper "RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior". Paper link: https://arxiv.org/abs/2010.00029

My solutions to the exercises in "Data Analysis Using Regression and Multilevel/Hierarchical Models" by Andrew Gelman and Jennifer Hill

Code for A Hierarchical Model for Data-to-Text Generation (Rebuffel, Soulier, Scoutheeten, Gallinari; ECIR 2020)

Hierarchical Attention Networks for Document Classification in Keras

Recursively tracks changes within a view model no matter how deeply nested the observables are or whether they are nested within dynamically created array elements.

Message Passing Attention Networks for Document Understanding

[ECCV2024] PartGLEE: A Foundation Model for Recognizing and Parsing Any Objects

[KDD 2020] Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding

Code for the paper "Fine-Grained Entity Typing in Hyperbolic Space"

[WWW 2023] The source code of "Learning Long- and Short-term Representations for Temporal Knowledge Graph Reasoning"

Fit models to data from unmarked animals using Stan. Uses a similar interface to the R package 'unmarked', while providing the advantages of Bayesian inference and allowing estimation of random effects.

An R package for estimating generalized additive mixed models with latent variables

Word Sense Disambiguation using Word Specific models, All word models and Hierarchical models in Tensorflow

[NeurIPS 2022] The implementation for the paper "Equivariant Graph Hierarchy-Based Neural Networks".

CRAN Task View: Mixed, Multilevel, and Hierarchical Models in R

PyTorch implementation of Metric-Guided Prototype Learning for hierarchical classification.

An in-development R package and a Bayesian hierarchical model jointly fitting multiple "local" wastewater data streams and "global" case count data to produce nowcasts and forecasts of both observations

Bayesian modelling of DNA methylation heterogeneity at single-cell resolution

This GitHub repository provides an implementation of the paper "MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network" . MAGNET is a state-of-the-art approach for multi-label text classification, leveraging the power of graph neural networks (GNNs) and attention mechanisms.

Deep exponential families for single-cell data.

🎓 Tidy multilevel modeling tools for academics