Results 61 to 70 of about 2,238,851 (367)
Concolic testing for deep neural networks [PDF]
Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, 2018 Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program. This paper presents the first concolic testing approach for Deep Neural Networks (DNNs). More specifically, we formalise coverage criteria for DNNs that have been studied in the literature, and then develop a coherent method for ...
Sun, Youcheng +5 more
openaire +7 more sources
Prediction of Shear Strength of Ultra High Performance Reinforced Concrete Deep Beams without Stirrups by Neural Network [PDF]
Eurasian Journal of Science and Engineering, 2017 : Shear strength of ultra high performance reinforced concrete deep beams without stirrups predicted by neural network models. The neural network model based on 233 beams from literatures considering different parameters such as span to depth ratio ...
Sinan Abdulkhaleq Yaseen +2 more
doaj +1 more source
Application of deep learning in recognition of accrued earnings management
Heliyon, 2023 We choose the sample data in Chinese capital market to compare the measurement effect of earnings management with Deep Belief Network, Deep Convolution Generative Adversarial Network, Generalized Regression Neural Network and modified Jones model by ...
Jia Li, Zhoutianyang Sun
doaj
Deep Petri nets of unsupervised and supervised learning
Measurement + Control, 2020 Artificial intelligence is one of the hottest research topics in computer science. In general, when it comes to the needs to perform deep learning, the most intuitive and unique implementation method is to use neural network.
Yi-Nan Lin +5 more
doaj +1 more source
On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression [PDF]
IEEE Signal Processing Letters, 2020 In this paper, we exploit the properties of mean absolute error (MAE) as a loss function for the deep neural network (DNN) based vector-to-vector regression.
Jun Qi +4 more
semanticscholar +1 more source
Deep learning in spiking neural networks [PDF]
Neural Networks, 2019 In recent years, deep learning has been a revolution in the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is trained in a supervised manner using backpropagation.
Kheradpisheh, Saeed Reza +5 more
openaire +4 more sources
Methods for Pruning Deep Neural Networks [PDF]
IEEE Access, 2022 Major revision that includes additional references and a new section for comparison of ...
Vadera, S, Ameen, SA
openaire +3 more sources
Deep neural network correlation learning mechanism for CT brain tumor detection
Neural computing & applications (Print), 2021 Modern medical clinics support medical examinations with computer systems which use Computational Intelligence on the way to detect potential health problems in more efficient way.
M. Woźniak, J. Siłka, M. Wieczorek
semanticscholar +1 more source
Activated Gradients for Deep Neural Networks [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2023 Deep neural networks often suffer from poor performance or even training failure due to the ill-conditioned problem, the vanishing/exploding gradient problem, and the saddle point problem. In this paper, a novel method by acting the gradient activation function (GAF) on the gradient is proposed to handle these challenges.
Mei Liu +4 more
openaire +4 more sources
Data‐driven performance metrics for neural network learning
International Journal of Adaptive Control and Signal Processing, EarlyView., 2023 Summary Effectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state ...
Angelo Alessandri +2 more
wiley +1 more source