Results 41 to 50 of about 911,793 (268)
StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity
IEEE Access, 2016 Deep neural networks are a branch in machine learning that has seen a meteoric rise in popularity due to its powerful abilities to represent and model high-level abstractions in highly complex data.
Mohammad Javad Shafiee +2 more
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Ambient backscatter communication-based smart 5G IoT network
EURASIP Journal on Wireless Communications and Networking, 2021 In this paper, we propose an ambient backscatter communication-based smart 5G IoT network. The network consists of two parts, namely a real-time data transmission system based on ambient backscatter communication and a real-time big data analysis system ...
Qiang Liu +3 more
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Deep-learning-based data page classification for holographic memory
, 2017 We propose a deep-learning-based classification of data pages used in holographic memory. We numerically investigated the classification performance of a conventional multi-layer perceptron (MLP) and a deep neural network, under the condition that ...
Hasegawa, Satoki +11 more
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, 2015 Proposed in the 1940s as a simplified model of the elementary computing unit in the human cortex, artificial neural networks (ANNs) have since been an active research area. Among the many evolutions of ANN, deep neural networks (DNNs) (Hinton, Osindero, and Teh 2006) stand out as a promising extension of the shallow ANN structure.
Mariette Awad, Rahul Khanna
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A new deep neural network for forecasting: Deep dendritic artificial neural network
Artificial Intelligence Review, 2023 Abstract Deep artificial neural networks have become a good alternative to classical forecasting methods in solving forecasting problems. Popular deep neural networks classically use additive aggregation functions in their cell structures.
Egrioglu, Erol, Bas, Eren
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Towards explainable deep neural networks (xDNN) [PDF]
Neural Networks, 2020 In this paper, we propose an elegant solution that is directly addressing the bottlenecks of the traditional deep learning approaches and offers a clearly explainable internal architecture that can outperform the existing methods, requires very little computational resources (no need for GPUs) and short training times (in the order of seconds).
Plamen Angelov, Eduardo Soares
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, 2018 Deep neural networks show great potential as solutions to many sensing application problems, but their excessive resource demand slows down execution time, pausing a serious impediment to deployment on low-end devices.
Abdelzaher, Tarek +6 more
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Search for deep graph neural networks
Information Sciences, 2023 Current GNN-oriented NAS methods focus on the search for different layer aggregate components with shallow and simple architectures, which are limited by the 'over-smooth' problem. To further explore the benefits from structural diversity and depth of GNN architectures, we propose a GNN generation pipeline with a novel two-stage search space, which ...
Guosheng Feng +2 more
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Deep Limits of Residual Neural Networks
, 2019 Neural networks have been very successful in many applications; we often, however, lack a theoretical understanding of what the neural networks are actually learning. This problem emerges when trying to generalise to new data sets.
Thorpe, Matthew, van Gennip, Yves
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Fault Diagnosis Method for Aerial Sensor Based on Deep Learning [PDF]
Jisuanji gongcheng, 2017 To solve the problem of over-fitting and limited generalization ability during sensor fault diagnosis by traditional neural network,a fault diagnosis method for aerial sensor based on deep belief network observer is proposed.Shallow layer neural network ...
ZHENG Xiaofei,GUO Chuang,YAO Bin,FENG Huaxin
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