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深度学习在高能物理领域中的应用

The application of deep learning in high energy physics

  • 摘要: 深度学习是一类通过多层信息抽象来学习复杂数据内在表示关系的机器学习算法。近年来,深度学习算法在物体识别和定位、语音识别等人工智能领域,取得了飞跃性进展。文章将首先介绍深度学习算法的基本原理及其在高能物理计算中应用的主要动机。然后结合实例综述卷积神经网络、递归神经网络和对抗生成网络等深度学习算法模型的应用。最后,文章将介绍深度学习与现有高能物理计算环境结合的现状、问题及一些思考。

     

    Abstract: Deep learning is a machine learning algorithm which learns intrinsic representations of complex data by multi-layer abstractions of information. It has dramatically improved the state-of-the-art in visual object recognition and detection, speech recognition, and many other artificial intelligence computing tasks. This paper will first introduce the basics of the algorithm and the motivations of its application in high energy physics computing. The applications of deep neural, convolutional neural and recursive neural networks as well as other models of deep learning in high energy physics will be summarized, together with several case studies. Finally, problems and potential solutions in the integration of deep learning with current high energy computing environments will be briefly mentioned.

     

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