Results 121 to 130 of about 2,238,851 (367)
Deep and Modular Neural Networks [PDF]
, 2015 In this chapter, we focus on two important areas in neural computation, i. e., deep and modular neural networks, given the fact that both deep and modular neural networks are among the most powerful machine learning and pattern recognition techniques for complex AI problem solving.
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Deep learning in neural networks: An overview [PDF]
Neural Networks, 2015 In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are ...
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Rethinking arithmetic for deep neural networks [PDF]
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2020 We consider efficiency in the implementation of deep neural networks. Hardware accelerators are gaining interest as machine learning becomes one of the drivers of high-performance computing. In these accelerators, the directed graph describing a neural network can be implemented as a directed graph describing a Boolean circuit.
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Molecular Oncology, EarlyView.This study presents a novel AI‐based diagnostic approach—comprehensive serum glycopeptide spectra analysis (CSGSA)—that integrates tumor markers and enriched glycopeptides from serum. Using a neural network model, this method accurately distinguishes liver and pancreatic cancers from healthy individuals.
Motoyuki Kohjima +6 more
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Deep residual neural-network-based robot joint fault diagnosis method
Scientific Reports, 2022 A data driven method-based robot joint fault diagnosis method using deep residual neural network (DRNN) is proposed, where Resnet-based fault diagnosis method is introduced.
Jinghui Pan, Lili Qu, Kaixiang Peng
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Safety Verification of Deep Neural Networks [PDF]
, 2017 Deep neural networks have achieved impressive experimental results in image classification, but can surprisingly be unstable with respect to adversarial perturbations, that is, minimal changes to the input image that cause the network to misclassify it. With potential applications including perception modules and end-to-end controllers for self-driving
Huang, X, Kwiatkowska, M, Wang, S, Wu, M
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Deep Dive into Deep Neural Networks with Flows
Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2020 Deep neural networks are becoming omnipresent in reason of their growing popularity in media and their daily use. However, their global complexity makes them hard to understand which emphasizes their black-box aspect and the lack of confidence given by their potential users.
Halnaut, Adrien +3 more
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FEBS Open Bio, EarlyView.This review highlights how foundation models enhance predictive healthcare by integrating advanced digital twin modeling with multiomics and biomedical data. This approach supports disease management, risk assessment, and personalized medicine, with the goal of optimizing health outcomes through adaptive, interpretable digital simulations, accessible ...
Sakhaa Alsaedi +2 more
wiley +1 more source
Vector quantization using k‐means clustering neural network
Electronics Letters, 2023 Vector Quantization (VQ) is a clustering problem in the fields of signal processing, source coding, information theory etc. Taking advantage of recent advances in the field of deep neural networks, this paper investigates the performance between VQ ...
Sio‐Kei Im, Ka‐Hou Chan
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Explaining deep neural networks
, 2020 Deep neural networks are becoming more and more popular due to their revolutionary success in diverse areas, such as computer vision, natural language processing, and speech recognition. However, the decision-making processes of these models are generally not interpretable to users.
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