Results 61 to 70 of about 1,645,295 (287)
Acta Dermato-Venereologica, 2022 Convolutional neural networks (CNNs) have shown promise in discriminating between invasive and in situ melanomas. The aim of this study was to analyse how a CNN model, integrating both clinical close-up and dermoscopic images, performed compared with 6 ...
Martin Gillstedt +8 more
doaj +1 more source
Automatic Pipeline Parallel Training Framework for General-purpose Computing Devices [PDF]
Jisuanji kexueTraining large-scale neural networks usually exceeds the memory and computing capacity of a single computing node,which requires distributed training using multiple nodes.Existing distributed deep learning frameworks are mainly designed for specific ...
ZHONG Zhenyu, LIN Yongliang, WANG Haotian, LI Dongwen, SUN Yufei, ZHANG Yuzhi
doaj +1 more source
Convolutional neural networks: an overview and application in radiology
Insights into Imaging, 2018 Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology.
R. Yamashita +3 more
semanticscholar +1 more source
Response : Computing with Neural Networks [PDF]
Science, 1987 Response: The intellectual thrust of our article (1) was to show the neurobiologist how a conceptual framework and methodology could be used to understand how a class of model neural circuits solve specific computational problems. In addition, we used examples of model circuit computation to illustrate the general problems neurobiologists face in ...
John J. Hopfield +2 more
openaire +3 more sources
Using spreadsheets as learning tools for computer simulation of neural networks [PDF]
SHS Web of Conferences, 2020 The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed.
Semerikov Serhiy +5 more
doaj +1 more source
Understanding the Modeling of Computer Network Delays using Neural Networks [PDF]
Big-DAMA@SIGCOMM, 2018 Recent trends in networking are proposing the use of Machine Learning (ML) techniques for the control and operation of the network. In this context, ML can be used as a computer network modeling technique to build models that estimate the network ...
Albert Mestres +3 more
semanticscholar +1 more source
Probabilistic classification of quality of service in wireless computer networks
ICT Express, 2019 There is an increasing reliance on wireless computer networks for communicating various types of time sensitive applications such as voice over internet protocol (VoIP). Quality of service (QoS) can play an important role in wireless computer networks as
Abdussalam Salama, Reza Saatchi
doaj +1 more source
Anti-periodic behavior for quaternion-valued delayed cellular neural networks
Advances in Difference Equations, 2021 In this manuscript, quaternion-valued delayed cellular neural networks are studied. Applying the continuation theorem of coincidence degree theory, inequality techniques and a Lyapunov function approach, a new sufficient condition that guarantees the ...
Zhenhua Duan, Changjin Xu
doaj +1 more source
Channel Pruning for Accelerating Very Deep Neural Networks [PDF]
IEEE International Conference on Computer Vision, 2017 In this paper, we introduce a new channel pruning method to accelerate very deep convolutional neural networks. Given a trained CNN model, we propose an iterative two-step algorithm to effectively prune each layer, by a LASSO regression based channel ...
Yihui He, Xiangyu Zhang, Jian Sun
semanticscholar +1 more source
4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks [PDF]
Computer Vision and Pattern Recognition, 2019 In many robotics and VR/AR applications, 3D-videos are readily-available input sources (a sequence of depth images, or LIDAR scans). However, in many cases, the 3D-videos are processed frame-by-frame either through 2D convnets or 3D perception algorithms.
C. Choy, JunYoung Gwak, S. Savarese
semanticscholar +1 more source