A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
Multipath Graph Convolutional Neural Networks
[article]
2021
arXiv
pre-print
Preserved Fulltext
In this work, we propose a novel Multipath Graph convolutional neural network that aggregates the output of multiple different shallow networks. ...
Recent research has focused on stacking multiple layers like in convolutional neural networks for the increased expressive power of graph convolution networks. ...
Convolution operation in GCNs is a generalization of the convolution operation used in convolution neural networks (CNNs) (Kipf and Welling 2016) . ...
arXiv:2105.01510v1
fatcat:mey2t3k72vegpg44foywvckhqm
ISCC 2020 Keyword Index
2020
2020 IEEE Symposium on Computers and Communications (ISCC)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
neural network
Convolutional Neural Network
convolutional neural networks
Convolutional Neural Networks
Cooperative Management
Coronavirus
Cortex-A9 MPCore
Cost function modification
Cost-Efficiency ...
gas consumption
Genetic algorithm
Genetic Algorithm
Glaucoma
Gossip
GPU
Grammatical Evolution
Graph Convolutional Network
Graph Neural Networks
grey model
group key management
guaranteed based ...
doi:10.1109/iscc50000.2020.9219679
fatcat:al6gjafwwneo5g5paprquzp7n4
GCLR: GNN based Cross Layer Optimization for Multipath TCP by Routing
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
INDEX TERMS Routing, multipath TCP, graph neural network, cross layer optimization, software defined networking. 17060 This work is licensed under a Creative Commons Attribution 4.0 License. ...
To address these problems, in this paper, firstly, a novel Graph Neural Network (GNN) based multipath routing model is proposed to explore the complications among links, paths, subflows and the MPTCP connection ...
Under the impetus of deep learning technologies, researchers propose the graph neural network [43] , which combines the thoughts of convolutional networks, cyclic networks, and deep auto-encoders. ...
doi:10.1109/access.2020.2966045
fatcat:ou4wkwyiqzeq7j5ionikghfqga
MRI based genomic analysis of glioma using three pathway deep convolutional neural network for IDH classification
2021
Turkish Journal of Electrical Engineering and Computer Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
A 3-pathway convolutional neural network was trained for IDH 9 classification. ...
The 13 results have demonstrated the multipath convolutional neural networks as state-of-the-art method with simple design to 14 predict IDH genotypes in glioma with auto-extraction of radiogenomic features ...
Our deep neural network-based work proposes multipath convolutional neural 29 network with the capability to auto-discriminate IDH types of glioma. ...
doi:10.3906/elk-2104-180
fatcat:gzolynp6vfgu3gtel6sg5bsxae
CISP-BMEI 2020 TOC
2020
2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Using Graph Theory Jing Ai, Tiantian Liu, Kexin Wang, Jian Zhang, Tianlin Huang ...
....407 Sentence Modeling via Graph Construction and Graph Neural Networks for Semantic Textual Similarity Ke Zhou ...
...........................................................231 Research on defect detection system of cloth based on convolutional neural network Zhang Qiyan, Li Mingjing, Yan Denghao, Yang Longbiao, Yu ...
doi:10.1109/cisp-bmei51763.2020.9263536
fatcat:7ulpvhnt35d2lg5dwzu4kexley
Automated Segmentation of Cervical Nuclei in Pap Smear Images using Deformable Multi-path Ensemble Model
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Other Versions
The approach adopts a U-shaped convolutional network as a backbone network, in which dense blocks are used to transfer feature information more effectively. ...
In this work, a method of automated cervical nuclei segmentation using Deformable Multipath Ensemble Model (D-MEM) is proposed. ...
In order to solve the aforementioned problems, we propose segmenting cervical nuclei via Deformable Multipath Ensemble Model (D-MEM) based on novel deep neural networks. ...
arXiv:1812.00527v2
fatcat:gy6fdq3dlndjlhbckrckvko57q
A Deep Learning Approach to Position Estimation from Channel Impulse Responses
2019
Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
However, in industrial environments where multipath propagation is predominant it is difficult to extract the correct ToF of the signal. ...
Our experiments show that our DL-based position estimation not only works well under harsh multipath propagation but also outperforms state-of-the-art approaches in line-of-sight situations. ...
Convolutional neural networks (CNN) define a special architecture of neural networks. ...
doi:10.3390/s19051064
fatcat:i5md3oe5ijfg5ok5ss7cj5mthy
3D imaging from multipath temporal echoes
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 accepted doi:10.1103/physrevlett.126.174301 arXiv:2011.09284v2 fatcat:s5lsadheajcovfmzybodd5nwrq
Numerical modelling and an information theoretic perspective prove the concept and provide insight into the role of the multipath information. ...
Multipath sensing has also been combined with Bayesian inference [23] and convolutional neural networks [24] to localise sonic sources. ...
neural network random. ...
arXiv:2011.09284v1
fatcat:dug3hnuaerdzza66i36ezzfjda
Neural RF SLAM for unsupervised positioning and mapping with channel state information
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
We present a neural network architecture for jointly learning user locations and environment mapping up to isometry, in an unsupervised way, from channel state information (CSI) values with no location ...
The neural network task is set prediction and is accordingly trained end-to-end. The proposed model learns an interpretable latent, i.e., user location, by just enforcing a physics-based decoder. ...
Many of probabilistic solutions to SLAM such as EKF-SLAM, Fast-SLAM, or Graph-SLAM have been used in multipath assisted positioning such as works in [2] , [3] , [4] . ...
arXiv:2203.08264v1
fatcat:zrs3ofa7v5c3tehgzo5vf6jwcy
Robust Ultra-wideband Range Error Mitigation with Deep Learning at the Edge
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
This article proposes an efficient representation learning methodology that exploits the latest advancement in deep learning and graph optimization techniques to achieve effective ranging error mitigation ...
Indeed, multipath effects, reflections, refractions, and complexity of the indoor radio environment can easily introduce a positive bias in the ranging measurement, resulting in highly inaccurate and unsatisfactory ...
Graph optimization (G.O.) and weight precision (W.P.) reduction further increase the capability of our already efficient neural network design helping to deal with energy, speed, size and cost constraints ...
arXiv:2011.14684v2
fatcat:iz5kqinydjd4rczdhlhogm2pi4
E-Commerce Picture Text Recognition Information System Based on Deep Learning
2022
Computational Intelligence and Neuroscience
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In terms of target recognition, compared with the traditional MWI-DenseNet neural network, the computation amount of the improved MWI DenseNet neural network is significantly reduced under different shunt ...
For the accuracy requirements of commodity image detection and classification, the FPN network is improved by DPFM ablation and RFM, so as to improve the detection accuracy of commodities by the network ...
But in the application of neural networks, increasing the network layers and amplifying the channels in the network feature graph, the effect of "widening and deepening" of convolutional neural network ...
doi:10.1155/2022/9474245
pmid:35106064
pmcid:PMC8801320
fatcat:wpygvyragrgt5h3kvba4hs7gfy
Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-valued Convolutional Networks
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
In response, guidelines of exact and approximate implementations of a complex-valued convolutional layer are provided for the design and analysis of convolutional networks for wireless PHY. ...
The proposed approach benefits from the expressive nature of complex-valued neural networks, which, however, currently lack support from popular deep learning platforms. ...
Regarding technical solutions, convolutional neural networks (CNNs) [4] , [5] , [16] , [23] , [24] , [28] are less often used than multilayer perceptron (MLP) [6] - [12] , [21] , [22] , [25 ...
arXiv:1810.07181v6
fatcat:nelcdghnbzhcrhrvcu2fmo5sve
MHA-Net: Multipath Hybrid Attention Network for building footprint extraction from high-resolution remote sensing imagery
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
The MHA-Net architecture consists of three components: the encoding network, multipath hybrid dilated convolution (HDC), and dense upsampling convolution (DUC). ...
We propose a novel multipath hybrid attention network (MHA-Net) to address these challenges. ...
In recent years, deep learning, especially the convolution neural networks (CNNs), has become one of the most prevalent methods in the computer vision field. ...
doi:10.1109/jstars.2021.3084805
fatcat:p3ngduovz5av3bednifsyfkne4
Road Scene Recognition of Forklift AGV Equipment Based on Deep Learning
2021
Processes
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
neural network model for the scene recognition of forklift AGV equipment in the warehouse environment. ...
network. ...
part of the convolutional neural network, and the information is used to expand the feature graph when the max-unpooling operation is carried out in the decoding network. ...
doi:10.3390/pr9111955
fatcat:g6525w4i5vgina7yvm52ly44t4
A Hybrid Approach based on Transfer and Ensemble Learning for Improving Performances of Deep Learning Models on Small Datasets
2021
Turkish Journal of Electrical Engineering and Computer Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In 7 this study, we propose a new approach that utilizes transfer learning and ensemble methods to increase the accuracy 8 rates of convolutional neural networks for classification tasks on small data ...
To this end, we generate different-sized 9 sub-networks by fragmenting an existing large pre-trained network then gather those networks to form an ensemble. 10 For ensemble scoring, we also suggest two ...
ImageNet classification with deep convolutional neural networks. Advances Dieleman S, Willett K, Dambre J. Rotation-invariant convolutional neural networks for galaxy morphology predic-tion. ...
doi:10.3906/elk-2102-101
fatcat:xtutyp7ydzgtzl42ze2lwvl5aq
« Previous
Showing results 1 — 15 out of 753 results