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We propose a scheme for training a neural network as an image classifier. The approach includes a very rapid unsupervised feature learning algorithm and a supervised technique. We show that convolving and downsampling clustered descriptors of image patches with each input image can provide more discriminative features compared to both pre-trained descriptors and randomly generated convolutional filters...
This paper presents a novel feature learning model for cyber security tasks. We propose to use Auto-encoders (AEs), as a generative model, to learn latent representation of different feature sets. We show how well the AE is capable of automatically learning a reasonable notion of semantic similarity among input features. Specifically, the AE accepts a feature vector, obtained from cyber security phenomena,...
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