6-2 基于分解的图神经网络可解释性.pdf

1、Data Analytics at Texas A&M LabDecomposition Based Explainability forDeep Neural NetworksMengnan Du Department of Computer Science&EngineeringTexas A&M UniversityEmail:dumengnantamu.eduhttps:/Data Analytics at Texas A&M Lab1Playing GoMedical DiagnosisScene UnderstandingVoice RecognitionData Analytic

2、s at Texas A&M Lab2What have been learned inside the models?Data Analytics at Texas A&M Lab3Explainability of DNNs enable us to explain the behaviorexplain the behavior of ablack-box DNN model in understandable termsunderstandable terms to humansMultilayer Perceptron(MLP)Multilayer Perceptron(MLP)Co

3、nvolutional Neural Networks(CNN)Convolutional Neural Networks(CNN)11Recurrent Neural Networks(RNN)Recurrent Neural Networks(RNN)catData Analytics at Texas A&M Lab4TraditionalTraditionalDeep LearningDeep LearningExplainableExplainableDeep LearningDeep Learning1 Fan Yang,Mengnan Du,Xia Hu.Evaluating e

4、xplanation without ground truth in interpretable machine learning.arXiv,2019.1Data Analytics at Texas A&M LabResearcher/developerEnd-users5DNNExplanationResearchersRefine ExplanationEnd-usersTrust Explanations are beneficial both to end-users and researchers For end-users:increase trust and transpar

5、ency For researchers/developers:diagnose why the model might fail and help them improve the modelData Analytics at Texas A&M Lab6Gradient based method Calculate gradient or variants of gradient using backpropogation Computational efficient“Eagle”Gradient base methods One backpropagation pass Data An

6、alytics at Texas A&M Lab7Perturbation based method Perturb the input,and feed perturbed input to model Observe the models prediction difference“Eagle”Perturbation base methods Perturb input,multiple backpropagation pass Data Analytics at Texas A&M Lab8“Understandable terms to humans”?(a)Prediction C