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    國家天元數學中部中心高性能計算系列報告 | 趙熙樂 教授(電子科技大學)
    發布時間:2021-10-14 16:58:11

    報告題目:High-Dimensional Data Recovery: When Deep Learning Meets Matrix/Tensor Decomposition

    報告時間:2021-10-18  14:30-15:30

    報告人:趙熙樂 教授  電子科技大學

    騰訊會議ID:599 206 385

    訪問此鏈接進入會議,或添加至會議列表:https://meeting.tencent.com/dm/EuexcjfwaVHw

    Abstract: Recently, low-rank tensor decomposition methods have received increasing attention for high-dimensional data recovery. However, only considering the low-rank structure of high-dimensional data is not sufficient for high-dimensional data recovery, especially for extremely complex imaging scenarios. In this talk, we will discuss how to bring into play the respective strengths of self-supervised learning and matrix/tensor decomposition for high-dimensional data recovery. Extensive numerical examples including inpainting, denoising, and snapshot compressed sensing are delivered to demonstrate the superiority of the suggested methods over state-of-the-art methods.




    Copyright 2019 ? 天元數學中部中心 National Tianyuan Mathematics Central Center

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