2 papers
cs.CL2020
Building Interpretable Interaction Trees for Deep NLP Models
Die Zhang, Huilin Zhou, Hao Zhang +6
This paper proposes a method to disentangle and quantify interactions among words that are encoded inside a DNN for natural language processing. We construct a tree to encode salie…
cs.LG2019
Interpretable CNNs for Object Classification
Quanshi Zhang, Xin Wang, Ying Nian Wu +2
This paper proposes a generic method to learn interpretable convolutional filters in a deep convolutional neural network (CNN) for object classification, where each interpretable f…