Learning Decision Trees Recurrently Through Communication
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021(2021)
摘要
Integrated interpretability without sacrificing the prediction accuracy of decision making algorithms has the potential of greatly improving their value to the user. Instead of assigning a label to an image directly, we propose to learn iterative binary sub-decisions, inducing sparsity and transparency in the decision making process. The key aspect of our model is its ability to build a decision tree whose structure is encoded into the memory representation of a Recurrent Neural Network jointly learned by two models communicating through message passing. In addition, our model assigns a semantic meaning to each decision in the form of binary attributes, providing concise, semantic and relevant rationalizations to the user. On three benchmark image classification datasets, including the large-scale ImageNet, our model generates human interpretable binary decision sequences explaining the predictions of the network while maintaining state-of-the-art accuracy.
更多查看译文
关键词
decision trees,integrated interpretability,prediction accuracy,decision making algorithms,iterative binary sub-decisions,inducing sparsity,transparency,decision making process,decision tree whose structure,memory representation,Recurrent Neural Network,message passing,model assigns,semantic meaning,binary attributes,relevant rationalizations,benchmark image classification datasets,human interpretable binary decision sequences
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要