5 papers
Towards Attributions of Input Variables in a Coalition
Xinhao Zheng, Huiqi Deng, Quanshi Zhang
This paper focuses on the fundamental challenge of partitioning input variables in attribution methods for Explainable AI, particularly in Shapley value-based approaches. Previous…
Where We Have Arrived in Proving the Emergence of Sparse Symbolic Concepts in AI Models
Qihan Ren, Jiayang Gao, Wen Shen +1
This study aims to prove the emergence of symbolic concepts (or more precisely, sparse primitive inference patterns) in well-trained deep neural networks (DNNs). Specifically, we p…
Explaining Generalization Power of a DNN Using Interactive Concepts
Huilin Zhou, Hao Zhang, Huiqi Deng +4
This paper explains the generalization power of a deep neural network (DNN) from the perspective of interactions. Although there is no universally accepted definition of the concep…
Does a Neural Network Really Encode Symbolic Concepts?
Mingjie Li, Quanshi Zhang
Recently, a series of studies have tried to extract interactions between input variables modeled by a DNN and define such interactions as concepts encoded by the DNN. However, stri…
Technical Note: Defining and Quantifying AND-OR Interactions for Faithful and Concise Explanation of DNNs
Mingjie Li, Quanshi Zhang
In this technical note, we aim to explain a deep neural network (DNN) by quantifying the encoded interactions between input variables, which reflects the DNN's inference logic. Spe…