| Explainable image classification: The journey so far and the road ahead V Kamakshi, NC Krishnan AI 4 (3), 620-651, 2023 | 34 | 2023 |
| Pace: Posthoc architecture-agnostic concept extractor for explaining cnns V Kamakshi, U Gupta, NC Krishnan 2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021 | 26 | 2021 |
| Mace: Model agnostic concept extractor for explaining image classification networks A Kumar, K Sehgal, P Garg, V Kamakshi, NC Krishnan IEEE Transactions on Artificial Intelligence 2 (6), 574-583, 2021 | 20 | 2021 |
| Evaluation of Saliency-based Explainability Method SZS Samuel, V Kamakshi, N Lodhi, NC Krishnan ICML Workshop on Theoretic Foundation, Criticism, and Application Trend of …, 2021 | 18 | 2021 |
| MAIRE-A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers R Sharma, N Reddy, V Kamakshi, NC Krishnan, S Jain International Cross-Domain Conference for Machine Learning and Knowledge …, 2021 | 8 | 2021 |
| Explainable Supervised Domain Adaptation V Kamakshi, NC Krishnan 2022 International Joint Conference on Neural Networks (IJCNN), 1-8, 2022 | 5 | 2022 |
| SCE: Shared Concept Extractor to Explain a CNN's Classification Dynamics V Kamakshi, N C Krishnan Proceedings of the 7th Joint International Conference on Data Science …, 2024 | 1 | 2024 |
| Towards Transparent Knowledge Graphs: A Position on Explainability in Link Prediction V Kamakshi, C Chaudhary | | 2025 |
| Concept-based Explanations for Convolutional Neural Network Predictions V Kamakshi | | 2023 |
Narayanan Chatapuram KrishnanIndian Institute of Technology PalakkadVerified email at iitpkd.ac.in
