3 papers
cs.CV2026
Local-to-Global Logical Explanations for Deep Vision Models
Bhavan Vasu, Giuseppe Raffa, Prasad Tadepalli
While deep neural networks are extremely effective at classifying images, they remain opaque and hard to interpret. We introduce local and global explanation methods for black-box…
cs.CV2025
Generating Part-Based Global Explanations Via Correspondence
Kunal Rathore, Prasad Tadepalli
Deep learning models are notoriously opaque. Existing explanation methods often focus on localized visual explanations for individual images. Concept-based explanations, while offe…
cs.LG2024
Chess Rating Estimation from Moves and Clock Times Using a CNN-LSTM
Michael Omori, Prasad Tadepalli
Current chess rating systems update ratings incrementally and may not always accurately reflect a player's true strength at all times, especially for rapidly improving players or v…