Results 11 to 20 of about 86,374 (257)
Experimental Quantum Embedding for Machine Learning
Advanced Quantum Technologies, 2022 Abstract The classification of big data usually requires a mapping onto new data clusters which can then be processed by machine learning algorithms by means of more efficient and feasible linear separators. Recently, Lloyd et al. have advanced the proposal to embed classical data into quantum ones: these live in the more complex ...
Gianani, I +8 more
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Optimising Resource Management for Embedded Machine Learning [PDF]
2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2020 Accepted at DATE ...
Xun, Lei +3 more
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Graph Embedded Extreme Learning Machine [PDF]
IEEE Transactions on Cybernetics, 2016 In this paper, we propose a novel extension of the extreme learning machine (ELM) algorithm for single-hidden layer feedforward neural network training that is able to incorporate subspace learning (SL) criteria on the optimization process followed for the calculation of the network's output weights. The proposed graph embedded ELM (GEELM) algorithm is
Alexandros Iosifidis +2 more
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Special Issue on Embedded Vision Architectures for Machine Learning [PDF]
Journal of Signal Processing Systems, 2022 Peer ...
François Berry +2 more
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Deep learning model construction for a semi-supervised classification with feature learning
Complex & Intelligent Systems, 2022 Several deep models were proposed in image processing, data interpretation, speech recognition, and video analysis. Most of these architectures need a massive proportion of training samples and use arbitrary configuration.
Sridhar Mandapati +4 more
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Pilot and Feasibility Studies, 2023 Background Stress levels and thus the risk of developing related physical and mental health conditions are rising worldwide. Dysfunctional beliefs contribute to the development of stress.
Marie Keinert +4 more
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Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy
Sensors, 2021 Terahertz imaging and time-domain spectroscopy have been widely used to characterize the properties of test samples in various biomedical and engineering fields.
Hochong Park, Joo-Hiuk Son
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A review on TinyML: State-of-the-art and prospects
Journal of King Saud University: Computer and Information Sciences, 2022 Machine learning has become an indispensable part of the existing technological domain. Edge computing and Internet of Things (IoT) together presents a new opportunity to imply machine learning techniques at the resource constrained embedded devices at ...
Partha Pratim Ray
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Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends
IEEE Open Journal of Industry Applications, 2023 This review article systematically summarizes the existing literature on utilizing machine learning (ML) techniques for the control and monitoring of electric machine drives.
Shen Zhang +2 more
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Electrostatic Embedding of Machine Learning Potentials
Journal of Chemical Theory and Computation, 2022 This work presents a variant of an electrostatic embedding scheme that allows the embedding of arbitrary machine learned potentials trained on molecular systems in vacuo. The scheme is based on physically motivated models of electronic density and polarizability, resulting in a generic model without relying on an exhaustive training set.
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