RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

Efficient random sampling via linear interpolation.

Fast Pseudo Random Number Generators for R

Null Models for Directed Hypergraphs

Fast Generation of von Mises-Fisher Distributed Pseudo-Random Vectors

Alpha stable and sub-Gaussian distributions in Julia

This paper proposes an alternative data-driven hap- tic modeling method of homogeneous deformable objects based on a CatBoost approach – a variant of gradient boosting machine learning approach. In this approach, decision trees are trained sequentially to learn the required mapping function for modeling the objects.

Collection of python scripts

Hyperparameter optimization in Julia.

PROSAC algorithm in python

[ICLR 2020] NAS evaluation is frustratingly hard

A Zig Module for Probability Distributions

An introduction to Monte Carlo methods by estimating π. This code comes in the form of a Python program.

In this project, I used a dataset containing the historical lending activity from a peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers.

CS404 Artificial Intelligence final project. This project is based on the Pneumonia Images dataset found on Kaggle. The goal was to classify the images using classic Artificial Neural Networks.

Code for the paper "Bavarian: Betweenness Centrality Approximation with Variance-Aware Rademacher Averages", by Chloe Wohlgemuth, Cyrus Cousins, and Matteo Riondato, appearing in ACM KDD'21 and ACM TKDD'23

Output randomly sampled lines from input stream or file

A nodejs module for randomly select elements.

A machine learning project to predict Customers/Clients into correct segment to provide promotional information or for product advertising.