图 书 馆
凯特:K——文本自动编码器KATE: K-Competitive Autoencoder for Text |
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| 课程网址: | http://videolectures.net/kdd2017_chen_autoencoder_for_text/ |
| 主讲教师: | Yu Chen |
| 开课单位: | 视频讲座网 |
| 开课时间: | 2017-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | 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. |
| 关 键 词: | 图像数据; 文本数据; 自编码器 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-12-02：chenxin01 |
| 最后编审: | 2022-12-02：chenxin01 |
| 阅读次数: | 27 |