822 citations · 899 across the 14 of their papers we have counts for
17 papers
A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation
Sarah E. Wessinger, Leslie N. Smith, Jacob Gull +3
Accurately estimating the refractive environment over multiple frequencies within the marine atmospheric boundary layer is crucial for the effective deployment of radar technologie…
Symmetry constrained neural networks for detection and localization of damage in metal plates
James Amarel, Christopher Rudolf, Athanasios Iliopoulos +2
The present paper is concerned with deep learning techniques applied to detection and localization of damage in a thin aluminum plate. We used data collected on a tabletop apparatu…
Cyclical Focal Loss
Leslie N. Smith
The cross-entropy softmax loss is the primary loss function used to train deep neural networks. On the other hand, the focal loss function has been demonstrated to provide improved…
General Cyclical Training of Neural Networks
Leslie N. Smith
This paper describes the principle of "General Cyclical Training" in machine learning, where training starts and ends with "easy training" and the "hard training" happens during th…
FROST: Faster and more Robust One-shot Semi-supervised Training
Helena E. Liu, Leslie N. Smith
Recent advances in one-shot semi-supervised learning have lowered the barrier for deep learning of new applications. However, the state-of-the-art for semi-supervised learning is s…
Building One-Shot Semi-supervised (BOSS) Learning up to Fully Supervised Performance
Leslie N. Smith, Adam Conovaloff
Reaching the performance of fully supervised learning with unlabeled data and only labeling one sample per class might be ideal for deep learning applications. We demonstrate for t…