Search results for "autoencoders"

showing 3 items of 3 documents

Open Set Audio Classification Using Autoencoders Trained on Few Data.

2020

Open-set recognition (OSR) is a challenging machine learning problem that appears when classifiers are faced with test instances from classes not seen during training. It can be summarized as the problem of correctly identifying instances from a known class (seen during training) while rejecting any unknown or unwanted samples (those belonging to unseen classes). Another problem arising in practical scenarios is few-shot learning (FSL), which appears when there is no availability of a large number of positive samples for training a recognition system. Taking these two limitations into account, a new dataset for OSR and FSL for audio data was recently released to promote research on solution…

Computer scienceOpen set02 engineering and technologylcsh:Chemical technologyMachine learningcomputer.software_genreBiochemistryArticleAnalytical ChemistrySet (abstract data type)open set recognition020204 information systemsaudio classificationautoencoders0202 electrical engineering electronic engineering information engineeringFeature (machine learning)lcsh:TP1-1185few-shot learningElectrical and Electronic EngineeringRepresentation (mathematics)Instrumentationbusiness.industryopen set classificationPerceptronClass (biology)AutoencoderAtomic and Molecular Physics and OpticsEmbedding020201 artificial intelligence & image processingArtificial intelligenceTransfer of learningbusinesscomputerSensors (Basel, Switzerland)
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Quantum Machine Learning: A tutorial

2021

This tutorial provides an overview of Quantum Machine Learning (QML), a relatively novel discipline that brings together concepts from Machine Learning (ML), Quantum Computing (QC) and Quantum Information (QI). The great development experienced by QC, partly due to the involvement of giant technological companies as well as the popularity and success of ML have been responsible of making QML one of the main streams for researchers working on fuzzy borders between Physics, Mathematics and Computer Science. A possible, although arguably coarse, classification of QML methods may be based on those approaches that make use of ML in a quantum experimentation environment and those others that take…

SpeedupTheoretical computer scienceQuantum machine learningComputer scienceCognitive NeuroscienceQuantum reinforcement learningQuantum computingFuzzy logicPopularityComputer Science ApplicationsComputational speed-upDevelopment (topology)Artificial IntelligenceQuantum clusteringQuantum informationQuantumQuantum-inspired learning algorithmsQuantum computerQuantum autoencoders
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Encryption and Generation of Images for Privacy-Preserving Machine Learning in Smart Manufacturing

2023

Current advances in machine (deep) learning and the exponential growth of data collected by and shared between smart manufacturing processes give a unique opportunity to get extra value from that data. The use of public machine learning services actualizes the issue of data privacy. Ordinary encryption protects the data but could make it useless for the machine learning objectives. Therefore, “privacy of data vs. value from data” is the major dilemma within the privacy preserving machine learning activity. Special encryption techniques or synthetic data generation are being in focus to address the issue. In this paper, we discuss a complex hybrid protection algorithm, which assumes sequenti…

data privacyIndustry 4.0anonymizationimage processingtietosuojakoneoppiminensalausautoencoderssyntetic data generationGeneral Earth and Planetary SciencesvalmistustekniikkakonenäköteollisuusanonymiteettiGeneral Environmental ScienceProcedia Computer Science
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