深度学习在高能物理领域中的应用

发布时间：2018-03-12 17:41

  本文选题：深度学习　切入点：人工智能　出处：《物理》2017年09期 　论文类型：期刊论文

【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]:Depth learning is a kind of machine learning algorithm which can learn the internal representation of complex data by multi-layer information abstraction. In recent years, depth learning algorithm has been applied in artificial intelligence fields such as object recognition and location, speech recognition and so on. This paper first introduces the basic principle of depth learning algorithm and the main motivation of its application in high energy physics computation, and then summarizes the convolution neural network with examples. The application of depth learning algorithm models such as recurrent neural networks and confrontation generating networks. Finally, this paper will introduce the current situation, problems and some thoughts about the combination of depth learning with the existing high energy physics computing environment.
【作者单位】： 中国科学院高能物理研究所计算中心;
【分类号】：O572;TP18
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本文编号：1602626