arXiv:2401.17200
공식 코드 · Papers with Code
NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble Techniques
원문 초록
This paper presents a comprehensive comparative analysis of explainable artificial intelligence (XAI) ensembling methods. Our research brings three significant contributions. Firstly, we introduce a novel ensembling method, NormEnsembleXAI, that leverages minimum, maximum, and average functions in conjunction with normalization techniques to enhance interpretability. Secondly, we offer insights into the strengths and weaknesses of XAI ensemble methods. Lastly, we provide a library, facilitating the practical implementation of XAI ensembling, thus promoting the adoption of transparent and interpretable deep learning models.
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Communicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano Things
midr: Learning from Black-Box Models by Maximum Interpretation Decomposition
Do Protein Transformers Have Biological Intelligence?
TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation
Addressing the Scarcity of Benchmarks for Graph XAI
Heart2Mind: Human-Centered Contestable Psychiatric Disorder Diagnosis System using Wearable ECG Monitors
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