3 papers
cs.CV2025
Interpretability-Aware Pruning for Efficient Medical Image Analysis
Nikita Malik, Pratinav Seth, Neeraj Kumar Singh +2
Deep learning has driven significant advances in medical image analysis, yet its adoption in clinical practice remains constrained by the large size and lack of transparency in mod…
cs.LG2025
xai_evals : A Framework for Evaluating Post-Hoc Local Explanation Methods
Pratinav Seth, Yashwardhan Rathore, Neeraj Kumar Singh +2
The growing complexity of machine learning and deep learning models has led to an increased reliance on opaque "black box" systems, making it difficult to understand the rationale…
cs.LG2025
DLBacktrace: A Model Agnostic Explainability for any Deep Learning Models
Vinay Kumar Sankarapu, Chintan Chitroda, Yashwardhan Rathore +2
The rapid growth of AI has led to more complex deep learning models, often operating as opaque "black boxes" with limited transparency in their decision-making. This lack of interp…