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Explainable Artificial Intelligence (XAI)

Explainable Artificial Intelligence

Papers

Showing 76100 of 1041 papers

TitleStatusHype
Explainable AI Components for Narrative Map ExtractionCode1
BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial IntelligenceCode1
Explainable Deep Learning Methods in Medical Image Classification: A SurveyCode1
BALANCE: Bayesian Linear Attribution for Root Cause LocalizationCode1
Extracting human interpretable structure-property relationships in chemistry using XAI and large language modelsCode1
In-Context Explainers: Harnessing LLMs for Explaining Black Box ModelsCode1
From Attribution Maps to Human-Understandable Explanations through Concept Relevance PropagationCode1
BayLIME: Bayesian Local Interpretable Model-Agnostic ExplanationsCode1
A Wearable Device Dataset for Mental Health Assessment Using Laser Doppler Flowmetry and Fluorescence Spectroscopy SensorsCode1
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural NetworksCode1
Insights Into the Inner Workings of Transformer Models for Protein Function PredictionCode1
Automatic Extraction of Linguistic Description from Fuzzy Rule BaseCode1
Landscape of R packages for eXplainable Artificial IntelligenceCode1
Learning Support and Trivial Prototypes for Interpretable Image ClassificationCode1
Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanationsCode1
MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept AlignmentCode1
Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image RecognitionCode1
Calibrated Explanations for RegressionCode1
NeuroXAI: Adaptive, robust, explainable surrogate framework for determination of channel importance in EEG applicationCode1
Deep-BIAS: Detecting Structural Bias using Explainable AICode1
Explaining Predictive Uncertainty with Information Theoretic Shapley ValuesCode1
An Explainable AI Framework for Artificial Intelligence of Medical Things0
An Experimental Study of Quantitative Evaluations on Saliency Methods0
A general approach to compute the relevance of middle-level input features0
Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems0
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