resource-constrained-ml

This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.

Code for Stress and Affect Detection on Resource-Constrained Devices

Code for Optimized Arrhythmia Detection on Ultra-Edge Devices

Python implementation of ELM - with optimized speed on MKL-based platforms; Described in conference paper: Radu Dogaru, Ioana Dogaru, "Optimization of extreme learning machines for big data applications using Python", COMM-2018; Allows quantization of weight parameters in both layers and introduces a new and very effective hidden layer nonlinearity (absolute value)

Embedded Vision for MVS in IoT

This repository is devoted to the development of the facial emotion recognition (FER) system as a final bachelor project at the TU/e. Realised by Blazej Manczak. Supervisors: Dr. Laura Astola (Accenture) and Dr. Vlado Menkovski (TU/e)

A proof of concept implementation of a Data Aware Neural Architecture Search.

subMFL: Compatible subModel Generation for Federated Learning in Device Heterogeneous Environment

Image classification using compressed deep neural network ported on resource-constrained platforms.

A Python implementation of the algorithm described in paper Radu Dogaru, Ioana Dogaru, "Optimized Super Fast Support Vector Classifiers Using Python and Acceleration of RBF Computations", (2018) ; There is no output layer learning only a relatively fast selection of support vectors in a RBF-layer optimized for speed. Faster than SVM

Models and their evaluation for paper: Radu Dogaru and Ioana Dogaru "RD-CNN: A Compact and Efficient Convolutional Neural Net for Sound Classification ", ISETC-2020