Results 261 to 270 of about 5,483,449 (298)

Convolutional Recurrent Neural Networks for Text Classification

Journal of Database Management, 2021
Recurrent neural network (RNN) and convolutional neural network (CNN) are two prevailing architectures used in text classification. Traditional approaches combine the strengths of these two networks by straightly streamlining them or linking features extracted from them. In this article, a novel approach is proposed to maintain the strengths of RNN and
Shengfei Lyu, Jiaqi Liu
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Recurrent Convolutional Neural Networks for Text Classification

Proceedings of the AAAI Conference on Artificial Intelligence, 2015
Text classification is a foundational task in many NLP applications. Traditional text classifiers often rely on many human-designed features, such as dictionaries, knowledge bases and special tree kernels. In contrast to traditional methods, we introduce a recurrent convolutional neural network for text classification without human ...
Siwei Lai, Liheng Xu, Kang Liu 0001, Jun Zhao 0001   +3 more
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Convolutional Recurrent Neural Networks for Text Classification

2019 International Joint Conference on Neural Networks (IJCNN), 2019
Text classification is an important task in natural language processing with wide applications. Traditional text classification methods manually extract the features which are later fed into the classifier for training. Recent researchers have employed convolutional neural networks or recurrent neural networks for text classification motivated by the ...
Ruishuang Wang, Zhao Li, Jian Cao, Tong Chen 0005, Lei Wang   +4 more
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Recurrent convolutional neural network for object recognition

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
In recent years, the convolutional neural network (CNN) has achieved great success in many computer vision tasks. Partially inspired by neuroscience, CNN shares many properties with the visual system of the brain. A prominent difference is that CNN is typically a feed-forward architecture while in the visual system recurrent connections are abundant ...
Ming Liang, Xiaolin Hu
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Convolutional Recurrent Neural Networks for Knowledge Tracing

2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2020
Knowledge Tracing (KT) is a task that aims to assess students' mastery level of knowledge and predict their performance over questions, which has attracted widespread attention over the years. Recently, an increasing number of researches have applied deep learning techniques to knowledge tracing and have made a huge success over traditional Bayesian ...
Wei Wang 0012, Tieyuan Liu, Liang Chang 0003, Tianlong Gu, Xuemei Zhao   +4 more
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Recurrent convolutional neural network for video classification

2016 IEEE International Conference on Multimedia and Expo (ICME), 2016
Video classification is more difficult than image classification since additional motion feature between image frames and amount of redundancy in videos should be taken into account. In this work, we proposed a new deep learning architecture called recurrent convolutional neural network (RCNN) which combines convolution operation and recurrent links ...
Zhenqi Xu, Jiani Hu, Weihong Deng
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Convolutional Recurrent Neural Networks for Computer Network Analysis

2019
The paper proposes a method of computer network user detection with recurrent neural networks. We use long short-term memory and gated recurrent unit neural networks. To present URLs from computer network sessions to the neural networks, we add convolutional input layers. Moreover, we transform requested URLs by one-hot character-level encoding.
Jakub Nowak, Marcin Korytkowski, Rafal Scherer   +2 more
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Convolutional decoding using recurrent neural networks

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 2003
We show how recurrent neutral network (RNN) convolutional decoders can be derived. As an example, we derive the RNN decoder for 1/2 rate code with constraint length 3. The derived RNN decoder is tested in Gaussian channel and the results are compared to results of optimal Viterbi decoder.
Ari Hämäläinen, Jukka Henriksson
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Recurrent convolutional neural network for speech processing

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
Different neural networks have exhibited excellent performance on various speech processing tasks, and they usually have specific advantages and disadvantages. We propose to use a recently developed deep learning model, recurrent convolutional neural network (RCNN), for speech processing, which inherits some merits of recurrent neural network (RNN) and
Yue Zhao 0006, Xingyu Jin, Xiaolin Hu
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Convolutional and Recurrent Neural Networks

2018
In the previous chapters, you have looked at fully connected networks and all the problems encountered while training them. The network architecture we have used, one in which each neuron in a layer is connected to all neurons in the previous and following layer, is not really good at many fundamental tasks, such as image recognition, speech ...
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