1 citations · 1 across the 7 of their papers we have counts for
4 papers · 1 filter
Are Objective Explanatory Evaluation metrics Trustworthy? An Adversarial Analysis
Prithwijit Chowdhury, Mohit Prabhushankar, Ghassan AlRegib +1
Explainable AI (XAI) has revolutionized the field of deep learning by empowering users to have more trust in neural network models. The field of XAI allows users to probe the inner…
Explaining Representation Learning with Perceptual Components
Yavuz Yarici, Kiran Kokilepersaud, Mohit Prabhushankar +1
Self-supervised models create representation spaces that lack clear semantic meaning. This interpretability problem of representations makes traditional explainability methods inef…
Taxes Are All You Need: Integration of Taxonomical Hierarchy Relationships into the Contrastive Loss
Kiran Kokilepersaud, Yavuz Yarici, Mohit Prabhushankar +1
In this work, we propose a novel supervised contrastive loss that enables the integration of taxonomic hierarchy information during the representation learning process. A supervise…
Counterfactual Gradients-based Quantification of Prediction Trust in Neural Networks
Mohit Prabhushankar, Ghassan AlRegib
The widespread adoption of deep neural networks in machine learning calls for an objective quantification of esoteric trust. In this paper we propose GradTrust, a classification tr…