政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/135329

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/135329

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题名:	原像分析結合視覺化分析作為探究XAI之可行性研究 The Study of Preimage Analysis With Visual Analytics For XAI
作者:	古聖釗 Gu, Sheng-Zhao
贡献者:	蔡瑞煌郁方 Tsaih, Rua-Huan Yu, Fang 古聖釗 Gu, Sheng-Zhao
关键词:	可解釋人工智慧單隱層前饋神經網路整流線性單元原像分析視覺化分析 Explainable Artificial Intelligence 1-hidden layer feed-forward neural network ReLU Preimage analysis Visual analytics
日期:	2021
上传时间:	2021-06-01 14:55:11 (UTC+8)
摘要:	本研究計畫探索可解釋人工智慧(Explainable Artificial Intelligence, XAI)議題。為了解決類神經網路(Artificial Neural Networks, ANN)的黑箱挑戰，本研究計畫將探討原像分析(Preimage analysis)的數學分析工具和視覺化分析(Visual analytics)是否可以用來打開黑箱。本研究將著眼於具有m個輸入節點，p個使用整流線性單元（Rectified Linear Unit, ReLU）激發函數的隱藏節點和一個使用線性激發函數的輸出節點的單隱層前饋神經網路(1-hidden Layer Feed-forward Neural network, 1HLNN)。近年來，ReLU在深度學習（Deep Learning）應用中被廣泛採用的原因是ReLU具有以下優點：（1）ReLU的計算成本低廉，因為它沒有復雜的數學運算，因此運算量較小，而訓練和執行的時間也較小；（2）線性是指輸入總合值變大時，該函數沒有“飽和”區域；（3）消失梯度問題可更容易地解決。還有，與深層神經網路（Deep Neural Networks, DNN）相比，1HLNN比較容易分析，比較容易打開其黑箱；而其推導得到的XAI結果可能可以擴展到DNN。因此，本研究計畫的重點之一乃在於探索具有ReLU激發函數的單隱層前饋神經網路的可解釋性。 This research explores Explainable Artificial Intelligence (XAI) issues. In order to solve the black box of Artificial Neural Networks (ANN), this research project will explore whether the mathematical analysis tools of Preimage analysis and Visual analytics can be used. We will focus on 1- hidden Layer Feed-forward Neural Network(1HLNN) with m input nodes, p hidden nodes using Rectified Linear Unit (ReLU) excitation functions. In recent years, ReLU has been widely used in deep learning applications because ReLU has the following advantages: (1) ReLU has low computational cost; (2) Linearity means that when the total input value becomes larger, the function does not have a "saturated" area; (3) The vanishing gradient problem can be solved more easily. Also, compared with Deep Neural Networks (DNN), 1HLNN is easier to analyze and open its black box; and it may be extended to DNN. Therefore, one of the points of this research is to explore the interpretability of 1HLNN with ReLU.
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描述:	碩士國立政治大學資訊管理學系 107356043
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0107356043
数据类型:	thesis
DOI:	10.6814/NCCU202100462
显示于类别:	[資訊管理學系] 學位論文

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