Results 11 to 20 of about 84,699 (302)
DL-Reg: A Deep Learning Regularization Technique using Linear Regression [PDF]
CoRR, 2020 Regularization plays a vital role in the context of deep learning by preventing deep neural networks from the danger of overfitting. This paper proposes a novel deep learning regularization method named as DL-Reg, which carefully reduces the nonlinearity of deep networks to a certain extent by explicitly enforcing the network to behave as much linear ...
Maryam Dialameh +2 more
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Deep Representation Learning for Speech Emotion Recognition [PDF]
, 2022 The success of machine learning (ML) algorithms generally depends on the quality of data representation or features. Good representations of the data make it easier to develop machine learning predictors or even deep learning (DL) classifiers.
Latif, Siddique
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Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review
IEEE Access, 2021 Deep learning (DL)-based PV Power Forecasting (PVPF) emerged nowadays as a promising research direction to intelligentize energy systems. With the massive smart meter integration, DL takes advantage of the large-scale and multi-source data ...
Mohamed Massaoudi +4 more
doaj +1 more source
Deep learning approach for real-time video streaming traffic classification [PDF]
, 2022 Video streaming services such as Amazon Prime Video, Netflix and YouTube, continue to be of enormous demands in everyday peoples’ lives. This enticed research in new mechanisms to provide a clear image of network usage and ensure better Quality of ...
Kanakis, Triantafyllos +4 more
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Generative adversarial network-based attenuation correction for 99mTc-TRODAT-1 brain SPECT
Frontiers in Medicine, 2023 BackgroundAttenuation correction (AC) is an important correction method to improve the quantification accuracy of dopamine transporter (DAT) single photon emission computed tomography (SPECT).
Yu Du +9 more
doaj +1 more source
BMC Bioinformatics, 2021 Background Deep learning is an active bioinformatics artificial intelligence field that is useful in solving many biological problems, including predicting altered epigenetics such as DNA methylation regions.
Pegah Mavaie +3 more
doaj +1 more source
IEEE Access, 2023 Deep Learning (DL) techniques are being used in various critical applications like self-driving cars. DL techniques such as Deep Neural Networks (DNN), Deep Reinforcement Learning (DRL), Federated Learning (FL), and Transfer Learning (TL) are prone to ...
Haider Ali +7 more
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WARC-DL: Scalable Web Archive Processing for Deep Learning
CoRR, 2022 Submitted to OSSYM 2022 - 4th International Open Search ...
Deckers, Niklas, Potthast, Martin
openaire +3 more sources
Quantum distributed deep learning architectures: Models, discussions, and applications
ICT Express, 2023 Although deep learning (DL) has already become a state-of-the-art technology for various data processing tasks, data security and computational overload problems often arise due to their high data and computational power dependency. To solve this problem,
Yunseok Kwak +7 more
doaj +1 more source
Deep learning approaches in flow visualization
Advances in Aerodynamics, 2022 With the development of deep learning (DL) techniques, many tasks in flow visualization that used to rely on complex analysis algorithms now can be replaced by DL methods.
Can Liu +6 more
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