GitHub

StatsPAI: a Python-native Stata/R replacement for applied causal inference

StatsPAI is for empirical researchers who would normally jump between Stata, R, and Python. Its goal is to make common Stata/R econometrics and causal-inference workflows feel native in Python: load a ...
GitHub

StatsPAI: Agent-Native Causal Inference & Econometrics Toolkit for Python

StatsPAI is a validation-tiered Python library for causal inference and applied econometrics. One import, 1,000+ registered functions across 80+ submodules (live count: python ...
C&EN

ORDerly: Data Sets and Benchmarks for Chemical Reaction Data

Machine learning has the potential to provide tremendous value to life sciences by providing models that aid in the discovery of new molecules and reduce the time for new products to come to market.
The Lancet

A deep learning model for novel systemic biomarkers in photographs of the external eye: a retrospective study

dOphthalmic Services and Eye Health Programs, Los Angeles County Department of Health Services, Los Angeles, CA, USA eDepartment of Ophthalmology, University of Southern California Keck School of ...
sei.cmu

Rust Vulnerability Analysis and Maturity Challenges

Wassermann, G., and Svoboda, D., 2023: Rust Vulnerability Analysis and Maturity Challenges. Carnegie Mellon University, Software Engineering Institute's Insights ...
Frontiers

U-RISC: An Annotated Ultra-High-Resolution Electron Microscopy Dataset Challenging the Existing Deep Learning Algorithms

Connectomics is a developing field aiming at reconstructing the connection of the neural system at the nanometer scale. Computer vision technology, especially deep learning methods used in image ...
Visual Studio Magazine

Preparing MNIST Image Data Text Files

Dr. James McCaffrey of Microsoft Research demonstrates how to fetch and prepare MNIST data for image recognition machine learning problems. Many machine learning problems fall into one of three ...