most citedExplanatory Paradigms in Neural Networks

3 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2022★ 2 cited

Gradient-Based Adversarial and Out-of-Distribution Detection

Jinsol Lee, Mohit Prabhushankar, Ghassan AlRegib

We propose to utilize gradients for detecting adversarial and out-of-distribution samples. We introduce confounding labels -- labels that differ from normal labels seen during trai…

eess.IV2022

Patient Aware Active Learning for Fine-Grained OCT Classification

Yash-yee Logan, Ryan Benkert, Ahmad Mustafa +2

This paper considers making active learning more sensible from a medical perspective. In practice, a disease manifests itself in different forms across patient cohorts. Existing fr…

eess.IV2022★ 2 cited

DECAL: DEployable Clinical Active Learning

Yash-yee Logan, Mohit Prabhushankar, Ghassan AlRegib

Conventional machine learning systems that operate on natural images assume the presence of attributes within the images that lead to some decision. However, decisions in medical d…

cs.CV2022

Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation

Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib

In seismic interpretation, pixel-level labels of various rock structures can be time-consuming and expensive to obtain. As a result, there oftentimes exists a non-trivial quantity…

cs.LG2022★ 3 cited

Explanatory Paradigms in Neural Networks

Ghassan AlRegib, Mohit Prabhushankar

In this article, we present a leap-forward expansion to the study of explainability in neural networks by considering explanations as answers to abstract reasoning-based questions.…