mlpack

Mentors: Student: kumar utkarsh

Training intricate machine learning models to achieve the desired level of proficiency can be time-consuming and require high computational resources. These challenges restrict the utilization of highly complex models in mlpack. To tackle this issue, one solution is to utilize pre-trained models available in the ONNX format. ONNX offers a standardized method for developers to define and exchange deep learning models across various frameworks like TensorFlow, PyTorch, and more.

Mentors: Student: Mark Fischinger

In Google Summer of Code 2024, I focused on optimizing key algorithms in the mlpack library, targeting both performance and memory usage across neural networks, clustering methods (KMeans, DBScan), and SVD. Highlights of the project include implementing FFT for faster convolution operations in neural networks, applying post-training quantization to reduce model sizes, and parallelizing KMeans and DBScan with OpenMP for improved execution speed. Additionally, I optimized memory usage and reduced binary sizes by refining the code structure. These enhancements have made mlpack faster and more efficient, contributing to its effectiveness for a wide range of machine learning applications.

Mentors: Student: Satyam Govind Shukla

Multi-objective optimization (MOO) plays a crucial role in decision-making across diverse fields, where conflicting objectives need to be balanced. This project proposes to enhance ensmallen's capabilities in MOO by implementing NSGA-III (Non-dominated Sorting Genetic Algorithm III) and AGE-MOEA (Adaptive Geometry Estimation-based Multi-Objective Evolutionary Algorithm). Furthermore, the project aims to add the Maf benchmark problems suite, which are based on real-world problems with varying properties, to benchmark the currently implemented optimizers based on both time taken and IGD score.

Mentors: Student: Andrew Furey

I will implement the yolov3-tiny model using mlpack. I will get Darknet-19 working, flesh out the API, build and train yolov3-tiny, publish the weights, and then test the model on constrained hardware, like the Raspberry PI 4, as well as testing it on my local GPU. I will then publish documentation on the implemented model, so that anybody can train and use the model, or download the pre-trained weights.

Mentors: Student: Abhimanyu Dayal

The goal of this project is to finish the XGBoost implementation inside of mlpack, followed by intense tuning, profiling, and improving such that it is competition ready. Additionally, I'll be implementing two more algorithms with similar implementation strategies - Gradient Boosting and LightGBM, and optimizing other tree based algorithms within mlpack.