Results 201 to 210 of about 22,071 (254)
pedQTNet: A Deep Learning Approach to Estimate Corrected QT Intervals from Multi-Lead Conventional ECG Waveforms in Pediatric Patients. [PDF]
J Med SystRuiz VM +7 more
europepmc +1 more source
Assessment of the Robustness of Deep Neural Networks (DNNs) [PDF]
, 2023 In the past decade, Deep Neural Networks (DNNs) have demonstrated outstanding performance in various domains. However, recently, some researchers have shown that DNNs are surprisingly vulnerable to adversarial attacks. For instance, adding a small, human-imperceptible perturbation to an input image can fool DNNs, enabling the model to make an ...
Mu, Ronghui +3 more
openaire +2 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Detecting DGA Using Deep Neural Networks (DNNs)
2019In recent days, malicious authors use domain generation algorithms so that they can easily evade blacklisting and heuristics mechanism. DGAs is used by a larger number of malware families to generate many pseudo-random domain names to connect to C2 server.
P. V. Jyothsna +3 more
openaire +2 more sources
SRS-DNN: a deep neural network with strengthening response sparsity
Neural Computing and Applications, 2019Inspired by the sparse mechanism of biological neural systems, an approach of strengthening response sparsity for deep learning is presented in this paper. Firstly, an unsupervised sparse pre-training process is implemented and a sparse deep network is begun to take shape.
Chen Qiao, Bin Gao, Yan Shi
openaire +2 more sources
TB-DNN: A Thin Binarized Deep Neural Network with High Accuracy
2020 22nd International Conference on Advanced Communication Technology (ICACT), 2020Deep neural networks (DNNs) have been widely used in many artificial intelligence (AI) applications. However, due to the huge demand for computing and storage resources as well as the high power consumption, deploying DNN models on embedded devices is full of challenges.
Jie Wang, Xi Jin 0002, Wei Wu
openaire +2 more sources
Deep neural network (DNN) modelling for prediction of the mode of delivery
European Journal of Obstetrics & Gynecology and Reproductive BiologyOne of the factors that worry obstetricians the most is the method of delivery. In recent years, the rate of caesarean sections has steadily climbed and now exceeds the threshold advised by medical organizations. Obstetricians typically lack the tools they need to assess whether vaginal delivery or a caesarean delivery is more appropriate. In this work,
Ananya, Kuanar +3 more
openaire +2 more sources
EC-DNN: A new method for parallel training of deep neural networks
Neurocomputing, 2018Abstract Parallelization framework has become a necessity to speed up the training of deep neural networks (DNN) recently. In the typical parallelization framework, called MA-DNN, the parameters of local models are periodically averaged to get a global model.
Shizhao Sun, Xiaoguang Liu 0001
openaire +2 more sources
On the training aspects of Deep Neural Network (DNN) for parametric TTS synthesis
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014Deep Neural Network (DNN), which can model a long-span, intricate transform compactly with a deep-layered structure, has recently been investigated for parametric TTS synthesis with a fairly large corpus (33,000 utterances) [6]. In this paper, we examine DNN TTS synthesis with a moderate size corpus of 5 hours, which is more commonly used for ...
Yao Qian +3 more
openaire +1 more source
Implementation and evaluation of deep neural networks (DNN) on mainstream heterogeneous systems
Proceedings of 5th Asia-Pacific Workshop on Systems, 2014Deep Neural Networks (DNN), with deep layers and very high dimension of parameters, have demonstrated break-through learning capability in machine learning area. These days DNN with Big Data input are leading a new direction in large scale object recognition.
Junli Gu +6 more
openaire +1 more source