hayesall/UniDAGs

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Created almost 4 years ago
Updated almost 4 years ago

hayesall/UniDAGs

hayesall

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🦄 Uniformly distributed samples of directed acyclic graphs for random Bayesian Networks

awesome-bayes-nets

⚗️ A curated list of Books, Research Papers, and Software for Bayesian Networks.

ffscraper

Yet another set of scraping tools for FanFiction.Net

MaliciousPortableExecutableDetection

Determining whether a portable executable file (.exe) is malicious or benign with comparative results for multiple ML algorithms: AdaBoost, DT, GNB, GradientBoosting, KNN, RF.

CLFanfictionSearchEngine

Search engine for Code Lyoko FanFiction, including some adapted scraping tools and indexers.

bn-rule-extraction

Rule Extraction from Bayesian Networks

Walk-ER

💾 Source code and 📓 TeX for AL Hayes, M Das, P Odom, and S Natarajan, "User Friendly Automatic Construction of Background Knowledge: Mode Construction from ER Diagrams." Knowledge Capture Conference 2017

ExportPublic.jl

🙊 ExportPublic.jl: Easily hide your implementation details

rfgb

🐍🚧 Experimental tool for SRL learning in Python. For something more stable, see: https://github.com/srlearn/srlearn

srlearn-StarAI-2020-workshop

StarAI Workshop 2020 - srlearn: A Python Library for Gradient-Boosted Statistical Relational Models

nuMoM2b_preprocessing

φ Preprocessing scripts to create reproducible partitions of the nuMoM2b data set. Part of the Indiana University Precision Health Initiative.

starling-theme

A Sphinx Theme for StARLinG Lab Documentation

Botnet-Buster

Final project for Professor Sriraam Natarajan's spring seminar on Statistical Relational Learning. Applying SRL to the problem of discerning botnet traffic 🤖 from normal traffic 👩‍💻

diabetes-time-series

Re-implementation of the UCI Diabetes Dataset from AIM 1994

DrugInteractionDiscovery

Indiana University ProHealth REU 2016. Discern which drugs interact with which other drugs. Information extraction on text documents and scraping tools for openFDA, PubMed, and various blogs.

babybear

🐼 It's like pandas, but tiny.