1 citations · 2 across the 5 of their papers we have counts for
5 papers
Using Deep Operators to Create Spatio-temporal Surrogates for Dynamical Systems under Uncertainty
Jichuan Tang, Patrick T. Brewick, Ryan G. McClarren +1
Spatio-temporal data, which consists of responses or measurements gathered at different times and positions, is ubiquitous across diverse applications of civil infrastructure. Whil…
Non-Generative Energy Based Models
Jacob Piland, Christopher Sweet, Priscila Saboia +2
Energy-based models (EBM) have become increasingly popular within computer vision. EBMs bring a probabilistic approach to training deep neural networks (DNN) and have been shown to…
Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models
Colton Crum, Patrick Tinsley, Aidan Boyd +5
The performance of convolutional neural networks has continued to improve over the last decade. At the same time, as model complexity grows, it becomes increasingly more difficult…
Improving Model's Focus Improves Performance of Deep Learning-Based Synthetic Face Detectors
Jacob Piland, Adam Czajka, Christopher Sweet
Deep learning-based models generalize better to unknown data samples after being guided "where to look" by incorporating human perception into training strategies. We made an obser…
On the Veracity of Cyber Intrusion Alerts Synthesized by Generative Adversarial Networks
Christopher Sweet, Stephen Moskal, Shanchieh Jay Yang
Recreating cyber-attack alert data with a high level of fidelity is challenging due to the intricate interaction between features, non-homogeneity of alerts, and potential for rare…