33 citations · 439 across the 68 of their papers we have counts for
10 papers · 1 filter
Explaining How a Neural Network Play the Go Game and Let People Learn
Huilin Zhou, Huijie Tang, Mingjie Li +3
The AI model has surpassed human players in the game of Go, and it is widely believed that the AI model has encoded new knowledge about the Go game beyond human players. In this wa…
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…
Can the Inference Logic of Large Language Models be Disentangled into Symbolic Concepts?
Wen Shen, Lei Cheng, Yuxiao Yang +2
In this paper, we explain the inference logic of large language models (LLMs) as a set of symbolic concepts. Many recent studies have discovered that traditional DNNs usually encod…
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…
HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation
Lu Chen, Siyu Lou, Keyan Zhang +2
The Shapley value is widely regarded as a trustworthy attribution metric. However, when people use Shapley values to explain the attribution of input variables of a deep neural net…
Understanding and Unifying Fourteen Attribution Methods with Taylor Interactions
Huiqi Deng, Na Zou, Mengnan Du +5
Various attribution methods have been developed to explain deep neural networks (DNNs) by inferring the attribution/importance/contribution score of each input variable to the fina…
Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts
Qihan Ren, Huiqi Deng, Yunuo Chen +2
In this paper, we focus on mean-field variational Bayesian Neural Networks (BNNs) and explore the representation capacity of such BNNs by investigating which types of concepts are…
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…