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Found 12 papers, 6 papers with code
Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network
Heterogeneous graph neural networks (HGNs) are prominent approaches to node classification tasks on heterogeneous graphs.
Two-Stage Holistic and Contrastive Explanation of Image Classification
The need to explain the output of a deep neural network classifier is now widely recognized.
Model Debiasing via Gradient-based Explanation on Representation
Machine learning systems produce biased results towards certain demographic groups, known as the fairness problem.
Consistency Regularization for Domain Generalization with Logit Attribution Matching
In this paper, we consider a third, lesser-known setting where a training domain is endowed with a collection of pairs of examples that share the same semantic information.
Towards Efficient Visual Simplification of Computational Graphs in Deep Neural Networks
A computational graph in a deep neural network (DNN) denotes a specific data flow diagram (DFD) composed of many tensors and operators.
ViT-CX: Causal Explanation of Vision Transformers
Despite the popularity of Vision Transformers (ViTs) and eXplainable AI (XAI), only a few explanation methods have been designed specially for ViTs thus far.
Tower Bridge Net (TB-Net): Bidirectional Knowledge Graph Aware Embedding Propagation for Explainable Recommender Systems
Recently, neural networks based models have been widely used for recommender systems (RS).
An Experimental Study of Quantitative Evaluations on Saliency Methods
It has been long debated that eXplainable AI (XAI) is an important technology for model and data exploration, validation, and debugging.
TDLS: A Top-Down Layer Searching Algorithm for Generating Counterfactual Visual Explanation
Explanation of AI, as well as fairness of algorithms' decisions and the transparency of the decision model, are becoming more and more important.
Resisting Out-of-Distribution Data Problem in Perturbation of XAI
Besides, our solution also resolves a fundamental problem with the faithfulness indicator, a commonly used evaluation metric of XAI algorithms that appears to be sensitive to the OoD issue.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Quantitative Evaluations on Saliency Methods: An Experimental Study
It has been long debated that eXplainable AI (XAI) is an important topic, but it lacks rigorous definition and fair metrics.
