Results 11 to 20 of about 652,316 (194)
Deep learning microscopy [PDF]
Optica, 2017 We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field-of-view and depth-of-field. After its training, the only input to this network is an image acquired using a regular optical microscope, without any changes to its design.
Yair Rivenson +5 more
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Alzheimer’s Research & Therapy, 2023 Background Developing a screening method for identifying individuals at higher risk of elevated brain amyloid burden is important to reduce costs and burden to patients in clinical trials on Alzheimer’s disease or the clinical setting.
Noriyuki Kimura +9 more
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Deep learning exotic hadrons [PDF]
Physical Review D, 2022 We perform the first model independent analysis of experimental data using Deep Neural Networks to determine the nature of an exotic hadron. Specifically, we study the line shape of the $P_c(4312)$ signal reported by the LHCb collaboration and we find that its most likely interpretation is that of a virtual state.
JPAC Collaboration +8 more
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Unconstrained neighbor selection for minimum reconstruction error-based K-NN classifiers
Complex & Intelligent Systems, 2023 It is essential to define more convincing and applicable classifiers for small datasets. In this paper, a minimum reconstruction error-based K-nearest neighbors (K-NN) classifier is proposed. We propose a new neighbor selection method.
Rassoul Hajizadeh
doaj +1 more source
Epilepsia Open, 2023 Objective As of 2022, 36 anti‐seizure medications (ASMs) have been licensed for the treatment of epilepsy, however, adverse effects (AEs) are commonly reported.
Kazuyuki Fukushima +14 more
doaj +1 more source
Deep learning in deep time [PDF]
Proceedings of the National Academy of Sciences, 2020 Digitized natural history records, now numbering in the billions (1), span widely across the tree of life and provide the foundation for numerous recent advances in biodiversity research (2, 3). Mechanistic insights are emerging for old questions, including how diversity has expanded and contracted through Earth’s history (4), how species have come to ...
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Deep learning in bioinformatics [PDF]
Briefings in Bioinformatics, 2016 In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields.
Seonwoo Min, Byunghan Lee, Sungroh Yoon
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Applied Sciences, 2021 To properly restore masonry cultural heritage sites, the materials used for retrofitting can have a critical effect, and this requires standards for traditional Korean brick and lime mortar to be examined.
Gayoon Lee +4 more
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Deep Learning in Cardiology [PDF]
IEEE Reviews in Biomedical Engineering, 2019 The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as
Paschalis Bizopoulos +1 more
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IEEE Transactions on Neural Networks and Learning Systems, 2018 In this paper, we propose a novel approach for efficient training of deep neural networks in a bottom-up fashion using a layered structure. Our algorithm, which we refer to as deep cascade learning, is motivated by the cascade correlation approach of Fahlman and Lebiere, who introduced it in the context of perceptrons.
Enrique S. Marquez +2 more
openaire +5 more sources