ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

Fit interpretable models. Explain blackbox machine learning.

Samples and Tools for Windows ML.

Samples for Intel® oneAPI Toolkits

Fast Best-Subset Selection Library

Framework of vectorized and distributed data analytics

APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.

Machine Learning Models Deployment using C++ Code Generation

A tool built on top of OpenFace to detect eye contact with babies.

An active vision system on the PR2 humanoid robot to dynamically detect objects via the head and arm cameras

Python-Wrapper for Francesco Parrella's OnlineSVR C++ implementation with scikit-learn-compatible interface.

A tiny C++ Time Series Database library designed for compatibility with the PyData Ecosystem.

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

This is my implementation of the 3D Pick and Place project for the Udacity Robotics Nanodegree. We perform multiple processes to segment a point cloud into its object components and use scikit-learn to do object recognition. A PR2 robot then performs a pick and place operation on the recognized objects in simulation with ROS and Gazebo.

Auto-ML based on a coevolutionary model.

pENC is a fast, hybrid C++/Python toolkit for parallel audio feature extraction and classification. It uses OpenCL (GPU) and Cython (CPU) for accelerated MFCC extraction, supports batch processing, and integrates with scikit-learn for machine learning. Ideal for research and large-scale audio analysis.

Scikit-learn-compatible python3 wrapper for Tsetlini - a Tsetlin Machine learning model

Deploys an optimized Decision Tree for Arrhythmia classification using Chapman ECG dataset on Arduino UNO board