resource-constrained-ml
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This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.
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May 20, 2024 - C++
Code for Stress and Affect Detection on Resource-Constrained Devices
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Jan 24, 2021 - Jupyter Notebook
Code for Optimized Arrhythmia Detection on Ultra-Edge Devices
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May 26, 2022 - Jupyter Notebook
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 nonlinear…
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Jul 26, 2021 - Python
Embedded Vision for MVS in IoT
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May 25, 2021 - Python
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)
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Jan 10, 2021 - Python
Federated Split Learning via Smashed Activation Gradient Estimation
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Aug 1, 2025 - Python
subMFL: Compatible subModel Generation for Federated Learning in Device Heterogeneous Environment
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Dec 30, 2024 - Jupyter Notebook
A proof of concept implementation of a Data Aware Neural Architecture Search.
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Dec 24, 2024 - Python
Image classification using compressed deep neural network ported on resource-constrained platforms.
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May 30, 2023 - Python
Ensemble Deep Random Vector Functional Link with Skip Connections (edRVFL-SC) No GPU required • 100× faster training
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Jul 18, 2025 - Python
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
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Aug 11, 2025 - Jupyter Notebook
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
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May 18, 2019 - Python
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