Topic: resource-constrained-ml Goto Github
Some thing interesting about resource-constrained-ml
Some thing interesting about resource-constrained-ml
resource-constrained-ml,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)
User: bmanczak
Home Page: https://colab.research.google.com/drive/1jh6illI4-wjseJVHWzCQpZpkVEmwPt8t#scrollTo=Exh6SKtuo-Of
resource-constrained-ml,A proof of concept implementation of a Data Aware Neural Architecture Search.
User: ekhao
resource-constrained-ml,This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.
Organization: microsoft
resource-constrained-ml,Code for Stress and Affect Detection on Resource-Constrained Devices
User: nandahkrishna
resource-constrained-ml,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)
User: radu-dogaru
resource-constrained-ml,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
User: radu-dogaru
resource-constrained-ml,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
User: radu-dogaru
resource-constrained-ml,Embedded Vision for MVS in IoT
User: tanveer-hussain
resource-constrained-ml,Code for Optimized Arrhythmia Detection on Ultra-Edge Devices
User: vishaln15
resource-constrained-ml,subMFL: Compatible subModel Generation for Federated Learning in Device Heterogeneous Environment
User: zeyneddinoz
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