Výsledky vyhledávání - "multitask autoencoder"

Direction-of-Arrival Estimation Based on Deep Neural Networks With Robustness to Array Imperfections Autor Liu, Zhang-Meng, Zhang, Chenwei, Yu, Philip S.

“…Lacking of adaptation to various array imperfections is an open problem for most high-precision direction-of-arrival (DOA) estimation methods. Machine…”
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Two-Phase Multitask Autoencoder-Based Deep Learning Framework for Subject-Independent EEG Motor Imagery Classification Autor Jin, Changgyun, Song, Andrew H., Kim, Seong-Eun

“…Electroencephalography (EEG)-based motor imagery (MI) has potential applications in diverse fields including rehabilitation, drone control, and virtual…”
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TWEN: EEG Emotion Recognition Model Based on Weakly Supervised Learning Framework with Two-Phase Multitask Autoencoder Autor Kim, Taewan, Jin, ChangGyun, Kim, Seong-Eun

“…Emotions significantly influence human cognition, behavior, and social interactions, making accurate recognition essential in Human-Computer Interaction (HCI)…”
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Active Learning for Speech Emotion Recognition Using Deep Neural Network Autor Abdelwahab, Mohammed, Busso, Carlos

“…Deep neural networks (DNNs) have consistently pushed the state-of-the-art performance in many fields, including speech emotion recognition. However, DNN-based…”
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Capacity estimation of lithium-ion battery with multi-task autoencoder and empirical mode decomposition Autor Sun, Qi, Cui, Fangshu, Shi, Mingrui

“…Capacity estimation of lithium-ion batteries is a commonly used method in health diagnosis and management. Its mainstream method involves using data-driven…”
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Universal Joint Feature Extraction for P300 EEG Classification Using Multi-Task Autoencoder Autor Ditthapron, Apiwat, Banluesombatkul, Nannapas, Ketrat, Sombat, Chuangsuwanich, Ekapol, Wilaiprasitporn, Theerawit

“…The process of recording electroencephalography (EEG) signals is onerous and requires massive storage to store signals at an applicable frequency rate. In this…”
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Sample selection using multi-task autoencoders in federated learning with non-IID data Autor Ardıç, Emre, Genç, Yakup

“…Federated learning is a machine learning paradigm in which multiple devices collaboratively train a model under the supervision of a central server while…”
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Within-Class Constraint Based Multi-task Autoencoder for One-Class Classification Autor Xie, Guojie, Wang, Tianlei, Liu, Dekang, Zhang, Wandong, Lai, Xiaoping

“…Autoencoders (AEs) have attracted much attention in one-class classification (OCC) based unsupervised anomaly detection. The AEs aim to learn the unity…”
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ANet: Autoencoder-Based Local Field Potential Feature Extractor for Evaluating an Antidepressant Effect in Mice After Administering Kratom Leaf Extracts Autor Nukitram, Jakkrit, Saengmolee, Wanumaidah, Chaisaen, Rattanaphon, Autthasan, Phairot, Sengnon, Narumon, Wungsintaweekul, Juraithip, Cheaha, Dania, Kumarnsit, Ekkasit, Sudhawiyangkul, Thapanun, Wilaiprasitporn, Theerawit

“…Kratom (KT) typically exerts antidepressant (AD) effects. However, evaluating which form of KT extracts possesses AD properties similar to the standard AD…”
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A Framework for Detecting Pulmonary Diseases from Lung Sound Signals Using a Hybrid Multi-Task Autoencoder-SVM Model Autor Orkweha, Khwanjit, Phapatanaburi, Khomdet, Pathonsuwan, Wongsathon, Jumphoo, Talit, Rattanasak, Atcharawan, Anchuen, Patikorn, Pinthurat, Watcharakorn, Uthansakul, Monthippa, Uthansakul, Peerapong

“…Research focuses on the efficacy of Multi-Task Autoencoder (MTAE) models in signal classification due to their ability to handle many tasks while improving…”
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