Results 271 to 280 of about 41,443 (305)

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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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 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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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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Improving the Donor Journey with Convolutional and Recurrent Neural Networks

2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020
Charities are constantly trying to decide what action they should take next with each constituent (person) in their database to encourage a donation. One way to make this decision is to use machine learning to build a model based on constituents' past actions, and use that model to choose which action to take next.
Greg Lee, Ajith Kumar Veera Raghavan, Mark Hobbs   +2 more
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Epileptic seizure prediction with recurrent convolutional neural networks

2017 25th Signal Processing and Communications Applications Conference (SIU), 2017
In this paper, the use of recurrent convolutional neural networks for predicting epileptic seizures is proposed. Effective methods for predicting epileptic seizures need to be developed for the design of diagnostic and therapeutic techniques that will prevent or mitigate epileptic seizures.
Ahmet Remzi Ozcan, Sarp Ertürk
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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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Mixed convolutional recurrent neural networks

Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing, 2019
In this work, we design a neural network named MCRNN for classifying signals with slight distinction. This model combines advantages of both Convolutional Neural Network and Recurrent Neural Network, allowing the network to distinguish between long signals with long time-frequency maps.
Wentao Wang, Canhui Liao, Quan Cheng, Pengju Wang   +3 more
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