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• W2951903140 abstract "Autoencoders have been successful in learning meaningful representations from image datasets. However, their performance on text datasets has not been widely studied. Traditional autoencoders tend to learn possibly trivial representations of text documents due to their confounding properties such as high-dimensionality, sparsity and power-law word distributions. In this paper, we propose a novel k-competitive autoencoder, called KATE, for text documents. Due to the competition between the neurons in the hidden layer, each neuron becomes specialized in recognizing specific data patterns, and overall the model can learn meaningful representations of textual data. A comprehensive set of experiments show that KATE can learn better representations than traditional autoencoders including denoising, contractive, variational, and k-sparse autoencoders. Our model also outperforms deep generative models, probabilistic topic models, and even word representation models (e.g., Word2Vec) in terms of several downstream tasks such as document classification, regression, and retrieval." @default.
• W2951903140 created "2019-06-27" @default.
• W2951903140 creator A5016010967 @default.
• W2951903140 creator A5019559411 @default.
• W2951903140 date "2017-05-04" @default.
• W2951903140 modified "2023-09-27" @default.
• W2951903140 title "KATE: K-Competitive Autoencoder for Text" @default.
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• W2951903140 hasPublicationYear "2017" @default.
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