41 citations · 162 across the 47 of their papers we have counts for
9 papers · 1 filter
Understanding and Leveraging the Learning Phases of Neural Networks
Johannes Schneider, Mohit Prabhushankar
The learning dynamics of deep neural networks are not well understood. The information bottleneck (IB) theory proclaimed separate fitting and compression phases. But they have sinc…
FOCAL: A Cost-Aware Video Dataset for Active Learning
Kiran Kokilepersaud, Yash-Yee Logan, Ryan Benkert +6
In this paper, we introduce the FOCAL (Ford-OLIVES Collaboration on Active Learning) dataset which enables the study of the impact of annotation-cost within a video active learning…
Clinical Trial Active Learning
Zoe Fowler, Kiran Kokilepersaud, Mohit Prabhushankar +1
This paper presents a novel approach to active learning that takes into account the non-independent and identically distributed (non-i.i.d.) structure of a clinical trial setting.…
Clinically Labeled Contrastive Learning for OCT Biomarker Classification
Kiran Kokilepersaud, Stephanie Trejo Corona, Mohit Prabhushankar +2
This paper presents a novel positive and negative set selection strategy for contrastive learning of medical images based on labels that can be extracted from clinical data. In the…
Exploiting the Distortion-Semantic Interaction in Fisheye Data
Kiran Kokilepersaud, Mohit Prabhushankar, Yavuz Yarici +2
In this work, we present a methodology to shape a fisheye-specific representation space that reflects the interaction between distortion and semantic context present in this data m…
Probing the Purview of Neural Networks via Gradient Analysis
Jinsol Lee, Charlie Lehman, Mohit Prabhushankar +1
We analyze the data-dependent capacity of neural networks and assess anomalies in inputs from the perspective of networks during inference. The notion of data-dependent capacity al…