MAIRE - A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers. - Rajat Sharma; Nikhil Reddy; Vidhya Kamakshi; Narayanan C. Krishnan; Shweta Jain 002 (CD-MAKE, 2021)

PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs. - Vidhya Kamakshi; Uday Gupta; Narayanan C. Krishnan (IJCNN, 2021)

Evaluation of Saliency-based Explainability Method. - Sam Zabdiel Sunder Samuel; Vidhya Kamakshi; Namrata Lodhi; Narayanan C. Krishnan (CoRR, 2021)

MACE: Model Agnostic Concept Extractor for Explaining Image Classification Networks. - Ashish Kumar; Karan Sehgal; Prerna Garg; Vidhya Kamakshi; Narayanan Chatapuram Krishnan (IEEE Trans. Artif. Intell., 2021)

Explainable Supervised Domain Adaptation. - Vidhya Kamakshi; Narayanan C. Krishnan (IJCNN, 2022)

SCE: Shared Concept Extractor to Explain a CNN's Classification Dynamics. - Vidhya Kamakshi; Narayanan C. Krishnan (COMAD/CODS, 2024)