Results 31 to 40 of about 2,238,851 (367)
Understanding the role of individual units in a deep neural network [PDF]
Proceedings of the National Academy of Sciences of the United States of America, 2020 Deep neural networks excel at finding hierarchical representations that solve complex tasks over large datasets. How can we humans understand these learned representations?
David Bau +5 more
semanticscholar +1 more source
Emergence of Network Motifs in Deep Neural Networks [PDF]
Entropy, 2020 Network science can offer fundamental insights into the structural and functional properties of complex systems. For example, it is widely known that neuronal circuits tend to organize into basic functional topological modules, called network motifs. In this article, we show that network science tools can be successfully applied also to the study of ...
Matteo Zambra +2 more
openaire +4 more sources
DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars [PDF]
International Conference on Software Engineering, 2017 Recent advances in Deep Neural Networks (DNNs) have led to the development of DNN-driven autonomous cars that, using sensors like camera, LiDAR, etc., can drive without any human intervention.
Yuchi Tian +3 more
semanticscholar +1 more source
A programmable diffractive deep neural network based on a digital-coding metasurface array
Nature Electronics, 2022 The development of artificial intelligence is typically focused on computer algorithms and integrated circuits. Recently, all-optical diffractive deep neural networks have been created that are based on passive structures and can perform complicated ...
Che Liu +10 more
semanticscholar +1 more source
Deep Neural Networks for Network Routing [PDF]
2019 International Joint Conference on Neural Networks (IJCNN), 2019 In this work, we propose a Deep Learning (DL) based solution to the problem of routing traffic flows in computer networks. Routing decisions can be made in different ways depending on the desired objective and, based on that objective function, optimal solutions can be computed using a variety of techniques, e.g.
Reis, João +5 more
openaire +3 more sources
Training deep quantum neural networks [PDF]
Nature Communications, 2020 AbstractNeural networks enjoy widespread success in both research and industry and, with the advent of quantum technology, it is a crucial challenge to design quantum neural networks for fully quantum learning tasks. Here we propose a truly quantum analogue of classical neurons, which form quantum feedforward neural networks capable of universal ...
Daniel Scheiermann +8 more
openaire +7 more sources
Research on Real-Time Face Recognition Algorithm Based on Lightweight Network
Jisuanji kexue yu tansuo, 2020 In order to achieve high-precision real-time face recognition on embedded and mobile devices, the advant-ages and disadvantages of common networks in face recognition are analyzed, and an efficient deep convolution neural network model Lightfacenet is ...
ZHANG Dian, WANG Haitao, JIANG Ying, CHEN Xing
doaj +1 more source
IEEE Access, 2022 Skin cancer is caused due to unusual development of skin cells and deadly type cancer. Early diagnosis is very significant and can avoid some categories of skin cancers, such as melanoma and focal cell carcinoma. The recognition and the classification of
A. Sharma +9 more
semanticscholar +1 more source
Live Programming Environment for Deep Learning with Instant and Editable Neural Network Visualization [PDF]
, 2020 Artificial intelligence (AI) such as deep learning has achieved significant success in a variety of application domains. Several visualization techniques have been proposed for understanding the overall behavior of the neural network defined by deep ...
Fukusato, Tsukasa +3 more
core +1 more source
Evaluating the Visualization of What a Deep Neural Network Has Learned [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2015 Deep neural networks (DNNs) have demonstrated impressive performance in complex machine learning tasks such as image classification or speech recognition. However, due to their multilayer nonlinear structure, they are not transparent, i.e., it is hard to
W. Samek +4 more
semanticscholar +1 more source