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XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
European Conference on Computer Vision, 2016We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary values resulting in 32\(\times \) memory saving.
Mohammad Rastegari +3 more
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Differential convolutional neural network
Neural Networks, 2019Convolutional neural networks with strong representation ability of deep structures have ever increasing popularity in many research areas. The main difference of Convolutional Neural Networks with respect to existing similar artificial neural networks is the inclusion of the convolutional part.
Sarıgül M., Ozyildirim B.M., Avci M.
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Modeling Relational Data with Graph Convolutional Networks
Extended Semantic Web Conference, 2017Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata) remain incomplete.
M. Schlichtkrull +5 more
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2019
In the last few years, convolutional neural networks (CNNs), along with recurrent neural networks (RNNs), have become a basic building block in constructing complex deep learning solutions for various NLP, speech, and time series tasks. LeCun first introduced certain basic parts of the CNN frameworks as a general NN framework to solve various high ...
James Whitaker, John Liu, Uday Kamath
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In the last few years, convolutional neural networks (CNNs), along with recurrent neural networks (RNNs), have become a basic building block in constructing complex deep learning solutions for various NLP, speech, and time series tasks. LeCun first introduced certain basic parts of the CNN frameworks as a general NN framework to solve various high ...
James Whitaker, John Liu, Uday Kamath
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MatConvNet: Convolutional Neural Networks for MATLAB
ACM Multimedia, 2014MatConvNet is an open source implementation of Convolutional Neural Networks (CNNs) with a deep integration in the MATLAB environment. The toolbox is designed with an emphasis on simplicity and flexibility.
A. Vedaldi, Karel Lenc
semanticscholar +1 more source
2019
In the previous chapters, we studied fully connected multilayer neural networks and their training, using backpropagation. In a typical multilayer neural network layer, with n input nodes and m neurons, we need to learn n × m parameters or weights.
Mahmoud Hamdy, Hisham El-Amir
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In the previous chapters, we studied fully connected multilayer neural networks and their training, using backpropagation. In a typical multilayer neural network layer, with n input nodes and m neurons, we need to learn n × m parameters or weights.
Mahmoud Hamdy, Hisham El-Amir
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2020
It is safe to say that one of the most powerful supervised deep learning models is convolutional neural networks (abbreviated as CNN or ConvNet). CNN is a class of deep learning networks, mostly applied to image data. However, CNN structures can be used in a variety of real-world problems including, but not limited to, image recognition, natural ...
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It is safe to say that one of the most powerful supervised deep learning models is convolutional neural networks (abbreviated as CNN or ConvNet). CNN is a class of deep learning networks, mostly applied to image data. However, CNN structures can be used in a variety of real-world problems including, but not limited to, image recognition, natural ...
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Coupled convolution layer for convolutional neural network
Neural Networks, 2016We propose a coupled convolution layer comprising multiple parallel convolutions with mutually constrained filters. Inspired by biological human vision mechanism, we constrain the convolution filters such that one set of filter weights should be geometrically rotated, mirrored, or be the negative of the other.
Masayuki Tanaka +3 more
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Convolution in Neural Networks
International ...Bhuyan, Bikram Pratim +3 more
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