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Lijunyi at
2019
Proceedings of the 13th International Workshop on Semantic Evaluation
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After the challenge, we train a series of neural network models such as convolutional neural network(CNN), TextCNN, long short-term memory(LSTM) and C-LSTM. ...
The final submission achieves 14th place in Task 9, SubTask A with the accuracy of 0.6776. ...
The difference between a cyclic neural network and a common neural network is that the convolutional neural network consists of a feature extrac-tor with a convolutional layer and a sub-sampling layer. ...
doi:10.18653/v1/s19-2212
dblp:conf/semeval/Li19
fatcat:h7nrspdshnc65ngzsg4ttzjala
Arguments for the Unsuitability of Convolutional Neural Networks for Non–Local Tasks
[article]
2021
arXiv
pre-print
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We will use this insight to reformulate a comparison task into a sorting task and use findings on sorting networks to propose a lower bound for the number of parameters a neural network needs to solve ...
Convolutional neural networks have established themselves over the past years as the state of the art method for image classification, and for many datasets, they even surpass humans in categorizing images ...
Hypothesis: Problems exhibiting low locality are ill fitted to be solved by Convolutional Neural Networks (CNNs) Intuitively, CNNs are ill suited to solve non-local tasks, since the convolutional part ...
arXiv:2102.11944v1
fatcat:6wgyd5ctzjhcfaidpo4zifosb4
Arguments for the unsuitability of convolutional neural networks for non–local tasks
2021
Neural Networks
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We will use this insight to reformulate a comparison task into a sorting task and use findings on sorting networks to propose a lower bound for the number of parameters a neural network needs to solve ...
Convolutional neural networks have established themselves over the past years as the state of the art method for image classification, and for many datasets, they even surpass humans in categorizing images ...
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper ...
doi:10.1016/j.neunet.2021.05.001
pmid:34000564
fatcat:aqnwm6ci3ner7ixnrx2tk5xfza
Application of the transfer learning to the medical images texture classification task
2020
E3S Web of Conferences
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Presented work investigates different approaches to solving image classification task with neural networks, specifically, using pre-processing for feature extraction and end-to-end application of convolutional ...
neural networks (CNN). ...
The main idea is that large convolutional neural networks that were trained on the big image sets with purpose of solving some competition task such as ImageNet Large Scale Visual Recognition Challenge ...
doi:10.1051/e3sconf/202022401020
fatcat:kkztebtz7rbyvbamkgq5hjn6eu
Comparative characteristics of the ability of convolutional neural networks to the concept of transfer learning
2022
Technology Audit and Production Reserves
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In conclusion, learning from convolutional neural networks using transfer learning techniques can be applied to a variety of classification tasks, ranging from numbers to space objects (stars and quasars ...
That is, the processes of transfer learning in convolutional neural networks in classification problems are considered. ...
One of the most effective architectures to solve this problem is convolutional neural networks (CNN). ...
doi:10.15587/2706-5448.2022.252695
fatcat:m6ca5uhhhvg3hmdnzamazef674
Character-level Convolutional Network for Text Classification Applied to Chinese Corpus
[article]
2016
arXiv
pre-print
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This is the first time that character-level convolutional neural network applied to text classification problem. ...
This article provides an interesting exploration of character-level convolutional neural network solving Chinese corpus text classification problem. ...
Acknowledgements Dataset: The dataset for proposed model. ...
arXiv:1611.04358v2
fatcat:43aujopy2vdi3kvkletay27hzq
The Application of Neural Network in Multiple Object Tracking
2018
DEStech Transactions on Computer Science and Engineering
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In this paper, we analyze the current trends and introduce the application of convolutional neural network and recurrent neural network in MOT. ...
Compared with the traditional MOT with the employment of hand-crafted features and the design of similarity function between detections, research in neural networks has shown competitive superiority and ...
MOT BASED ON CONVOLUTIONAL NEURAL NETWORK In view of the great advantages of Convolutional Neural Network (CNN) in feature expression, scholars try to use neural network to extract the features of the ...
doi:10.12783/dtcse/csse2018/24504
fatcat:qoa25xwrkbcjblyxcavb3obuie
Camera-Based Blind Spot Detection with a General Purpose Lightweight Neural Network
2019
Electronics
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Many new convolutional neural network (CNN) structures have been proposed and most of the networks are very deep in order to achieve the state-of-art performance when evaluated with benchmarks. ...
Subsequently, a series of experiments are conducted to design an efficient neural network by comparing some of the latest deep learning models. ...
Here, we hold the same network structure but change the setting of the building block. We propose four different blocks, corresponding to four different neural networks. ...
doi:10.3390/electronics8020233
fatcat:le3byzn2h5axrekdzwbii5h6im
Overview of the Three-dimensional Convolutional Neural Networks Usage in Medical Computer-aided Diagnosis Systems
2020
American Journal of Neural Networks and Applications
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This article overviews the three-dimensional convolutional neural networks and how they are different from their two-dimensional versions. ...
Most modern diagnosis systems work with three-dimensional medical images that cannot be processed by traditional two-dimensional convolutional neural networks to get high enough prediction results. ...
In three-dimensional convolution neural networks, the subsampling stage (pooling) is applied in the same manner as in the two-dimensional convolutional neural networks, except that it works with three ...
doi:10.11648/j.ajnna.20200602.12
fatcat:ipbyq6ipava7zld6htgtzszsva
Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning
[article]
2019
arXiv
pre-print
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At the same time, through comparative experiment, we proved that our proposed method gained considerable improvement in performance compared with single channel convolutional neural network air quality ...
In this paper, we propose a method for air quality measurement based on double-channel convolutional neural network ensemble learning to solve the problem of feature extraction for different parts of environmental ...
At the same time, we adopted dropout [15] with probability of 0.5 to prevent network overfitting at the last convolution layer of each channel convolutional neural network. ...
arXiv:1902.06942v3
fatcat:xobaseho25ezperksjyunkqyf4
Matrix Shuffle-Exchange Networks for Hard 2D Tasks
[article]
2020
arXiv
pre-print
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Convolutional neural networks have become the main tools for processing two-dimensional data. ...
We show that the Matrix Shuffle-Exchange network is well-suited for algorithmic and logical reasoning tasks on matrices and dense graphs, exceeding convolutional and graph neural network baselines. ...
ACKNOWLEDGMENTS We would like to thank the IMCS UL Scientific Cloud for the computing power and Leo Trukšans for the technical support. ...
arXiv:2006.15892v2
fatcat:gtzxxfpdc5hqrpgrijomku266i
Design of hybrid neural networks of the ensemble structure
2021
Eastern-European Journal of Enterprise Technologies
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To improve the accuracy of solving the classification problem in the processing of images, the ensemble structure of hybrid convolutional neural networks (HCNNs) has been proposed in the current work. ...
To minimize computational costs while maintaining the specified accuracy this work set tasks of both the generation of possible topology and structural-parametric synthesis of convolutional neural networks ...
However, convolutional networks, which are the most popular method of solving such a task, use the same amount of computing resources on all parts of the image. ...
doi:10.15587/1729-4061.2021.225301
fatcat:ze4ufr667ratjpuggp4bb4zv5m
Simultaneous Object Detection and Semantic Segmentation
[article]
2020
arXiv
pre-print
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There has been a lot of research to solve both tasks using Convolutional Neural Networks. These approaches give good results but are computationally demanding. ...
Otherwise it is not possible to meet the real-time requirements of automated vehicles. In this work, we propose a neural network architecture to solve both tasks simultaneously. ...
It also is not possible to execute different neural networks for every imaginable task at the same time. ...
arXiv:1905.02285v2
fatcat:o6xd3m6eifelnpemactokpcgs4
Deep learning long-range information in undirected graphs with wave networks
[article]
2018
arXiv
pre-print
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We compare wave to graph convolution, an architecture based on local aggregation, and find that wave learns three different graph-based tasks with greater efficiency and accuracy. ...
An implementation of the wave network, and example code for the maze problem are included in the tflon deep learning toolkit (https://bitbucket.org/mkmatlock/tflon). ...
The content is the sole responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. ...
arXiv:1810.12153v1
fatcat:yls72fvqqvefdjsctqf7zyuohy
Union-net: A deep neural network model adapted to small data sets
[article]
2020
arXiv
pre-print
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This model combines convolutional network units with different combinations of the same input to form a union module. Each union module is equivalent to a convolutional layer. ...
The output of each union module is fused and added as the input of the last convolutional layer to form a complex network with a 4-layer network structure. ...
This paper proposes the Union-net model. Convolutional network units with different combinations of the same input form a Union module. Each Union module is equivalent to a convolutional layer. ...
arXiv:2012.13044v1
fatcat:75aa7dpxtfaxfkldtzooo57t6i
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