Selected article for: "deep learning and end end"
Author: Xiang Bai; Cong Fang; Yu Zhou; Song Bai; Zaiyi Liu; Qianlan Chen; Yongchao Xu; Tian Xia; Shi Gong; Xudong Xie; Dejia Song; Ronghui Du; Chunhua Zhou; Chengyang Chen; Dianer Nie; Dandan Tu; Changzheng Zhang; Xiaowu Liu; Lixin Qin; Weiwei Chen
Title: Predicting COVID-19 malignant progression with AI techniques
  • Document date: 2020_3_23
    • ID: 50oy9qqy_29
    Snippet: Unlike the traditional predictive model using a hand-crafted feature extractor and shallow classifiers, our deep learning-based method using a multilayer perceptron combined with an LSTM to this predictive task, which attempts to learn high-level hierarchical features from mass data, and expands the search space of the features for specific tasks. Moreover, this method jointly optimizes the feature extraction network and classifier through an end.....
    Document: Unlike the traditional predictive model using a hand-crafted feature extractor and shallow classifiers, our deep learning-based method using a multilayer perceptron combined with an LSTM to this predictive task, which attempts to learn high-level hierarchical features from mass data, and expands the search space of the features for specific tasks. Moreover, this method jointly optimizes the feature extraction network and classifier through an end-to-end manner. All rights reserved. No reuse allowed without permission. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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