Results 31 to 40 of about 1,721,209 (309)
Text Hiding Using Artificial Neural Networks [PDF]
Engineering and Technology Journal, 2012 The growth of information technology and data transfer led to increase the data attacks, so that information security becomes an important issue to keep the data saved during information exchanges in computer networks.
Haider Tarish Haider +2 more
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Resilient Binary Neural Network
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Binary neural networks (BNNs) have received ever-increasing popularity for their great capability of reducing storage burden as well as quickening inference time. However, there is a severe performance drop compared with {real-valued} networks, due to its intrinsic frequent weight oscillation during training.
Xu, Sheng +7 more
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Uncertainty-aware Binary Neural Networks [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 Binary Neural Networks (BNN) are promising machine learning solutions for deployment on resource-limited devices. Recent approaches to training BNNs have produced impressive results, but minimizing the drop in accuracy from full precision networks is still challenging.
Junhe Zhao +4 more
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Stress detection using deep neural networks
BMC Medical Informatics and Decision Making, 2020 Background Over 70% of Americans regularly experience stress. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and thus is deeply detrimental to physiological health and psychological wellbeing.
Russell Li, Zhandong Liu
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Applied Sciences, 2022 Traveling salesman, linear ordering, quadratic assignment, and flow shop scheduling are typical examples of permutation-based combinatorial optimization problems with real-life applications.
Syeda M. Tahsien, Fantahun M. Defersha
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IE-Net: Information-Enhanced Binary Neural Networks for Accurate Classification
Electronics, 2022 Binary neural networks (BNNs) have been proposed to reduce the heavy memory and computation burdens in deep neural networks. However, the binarized weights and activations in BNNs cause huge information loss, which leads to a severe accuracy decrease ...
Rui Ding, Haijun Liu, Xichuan Zhou
semanticscholar +1 more source
Region-DH: Region-based Deep Hashing for Multi-Instance Aware Image Retrieval [PDF]
, 2020 This paper introduces an instance-aware hashing approach Region-DH for large-scale multi-label image retrieval. The accurate object bounds can significantly increase the hashing performance of instance features.
Mtope, Franck Romuald Fotso, Wei, Bo
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A Deep Learning Accelerator Based on a Streaming Architecture for Binary Neural Networks
IEEE Access, 2022 Deep neural networks (DNNs) have played an increasingly important role in various areas such as computer vision and voice recognition. While training and validation become gradually feasible with high-end general-purpose processors such as graphical ...
Quang Hieu Vo +4 more
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On Tractable Representations of Binary Neural Networks [PDF]
International Conference on Principles of Knowledge Representation and Reasoning, 2020 We consider the compilation of a binary neural network’s decision function into tractable representations such as Ordered Binary Decision Diagrams (OBDDs) and Sentential Decision Diagrams (SDDs).
Weijia Shi +3 more
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An adiabatic method to train binarized artificial neural networks
Scientific Reports, 2021 An artificial neural network consists of neurons and synapses. Neuron gives output based on its input according to non-linear activation functions such as the Sigmoid, Hyperbolic Tangent (Tanh), or Rectified Linear Unit (ReLU) functions, etc..
Yuansheng Zhao, Jiang Xiao
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