Results 91 to 100 of about 370,356 (314)
A survey of quantization methods for deep neural networks
工程科学学报, 2023 The study of deep neural networks has recently gained widespread attention in recent years, with many researchers proposing network structures that exhibit exceptional performance. A current trend in artificial intelligence (AI) technology involves using
Chun YANG +8 more
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Tumour–host interactions in Drosophila: mechanisms in the tumour micro‐ and macroenvironment
Molecular Oncology, EarlyView.This review examines how tumour–host crosstalk takes place at multiple levels of biological organisation, from local cell competition and immune crosstalk to organism‐wide metabolic and physiological collapse. Here, we integrate findings from Drosophila melanogaster studies that reveal conserved mechanisms through which tumours hijack host systems to ...
José Teles‐Reis, Tor Erik Rusten
wiley +1 more source
Towards an optimal set of initial weights for a Deep Neural Network architecture
, 2019 Modern neural network architectures are powerful models. They have been proven efficient in many fields, such as imaging and acoustic. However, these neural networks involve a long-running and time-consuming process.
Saadi, A, Belhadef, Hacene
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BMC Anesthesiology, 2021 Background Estimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under certain
Jian Zhan +6 more
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Molecular Oncology, EarlyView.Glioma cells mainly express the endothelin receptor EDNRB, while EDNRA is restricted to a perivascular tumor subpopulation. Endothelin signaling reduces glioma cell proliferation while promoting migration and a proneural‐to‐mesenchymal transition associated with poor prognosis. This pathway activates Ca2+, K+, ERK, and STAT3 signalings and is regulated
Donovan Pineau +36 more
wiley +1 more source
Deep Learning and Higher Degree F-Transforms: Interpretable Kernels Before and After Learning
International Journal of Computational Intelligence Systems, 2020 One of the current trends in the deep neural network technology consists in allowing a man–machine interaction and providing an explanation of network design and learning principles. In this direction, an experience with fuzzy systems is of great support.
Vojtech Molek, Irina Perfilieva
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Molecular Oncology, EarlyView.Keratin 19 (KRT19) is overexpressed in high‐grade serous ovarian cancer with high levels of Kallikrein‐related peptidases (KLK) 4–7 and is associated with poor survival. In vivo analyses demonstrate that elevated KRT19 increases peritoneal tumour burden.
Sophia Bielesch +13 more
wiley +1 more source
Deep Cognitive Neural Network (DCNN) [PDF]
, 2019 Embodiments of the present systems and methods may provide a more efficient and low-powered cognitive computational platform utilizing a deep cognitive neural network (DCNN), incorporating an architecture that integrates convolutional feedforward and ...
Hussain, Amir +3 more
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Deep process neural network for temporal deep learning
, 2014 Process neural network is widely used in modeling temporal process inputs in neural networks. Traditional process neural network is usually limited in structure of single hidden layer due to the unfavorable training strategies of neural network with ...
Xie, Kunqing +7 more
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Analysis of Internet Financial Risks Based on Deep Learning and BP Neural Network
, 2022 The study aims to analyze and forecast Internet financial risks based on the model based on deep learning and the Back Propagation Neural Network (BPNN).
Zhou, Shuai +4 more
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