139 citations · 214 across the 4 of their papers we have counts for
4 papers
Efficient Saliency Maps for Explainable AI
T. Nathan Mundhenk, Barry Y. Chen, Gerald Friedland
We describe an explainable AI saliency map method for use with deep convolutional neural networks (CNN) that is much more efficient than popular fine-resolution gradient methods. I…
Improvements to context based self-supervised learning
T. Nathan Mundhenk, Daniel Ho, Barry Y. Chen
We develop a set of methods to improve on the results of self-supervised learning using context. We start with a baseline of patch based arrangement context learning and go from th…
Sampled Image Tagging and Retrieval Methods on User Generated Content
Karl Ni, Kyle Zaragoza, Charles Foster +4
Traditional image tagging and retrieval algorithms have limited value as a result of being trained with heavily curated datasets. These limitations are most evident when arbitrary…
Large-Scale Deep Learning on the YFCC100M Dataset
Karl Ni, Roger Pearce, Kofi Boakye +4
We present a work-in-progress snapshot of learning with a 15 billion parameter deep learning network on HPC architectures applied to the largest publicly available natural image an…