65 citations · 108 across the 5 of their papers we have counts for
5 papers
Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling
Gregory P. Spell, Simiao Ren, Leslie M. Collins +1
We propose and show the efficacy of a new method to address generic inverse problems. Inverse modeling is the task whereby one seeks to determine the control parameters of a natura…
FLOP: Federated Learning on Medical Datasets using Partial Networks
Qian Yang, Jianyi Zhang, Weituo Hao +2
The outbreak of COVID-19 Disease due to the novel coronavirus has caused a shortage of medical resources. To aid and accelerate the diagnosis process, automatic diagnosis of COVID-…
Background Adaptive Faster R-CNN for Semi-Supervised Convolutional Object Detection of Threats in X-Ray Images
John B. Sigman, Gregory P. Spell, Kevin J Liang +1
Recently, progress has been made in the supervised training of Convolutional Object Detectors (e.g. Faster R-CNN) for threat recognition in carry-on luggage using X-ray images. Thi…
Toward Automatic Threat Recognition for Airport X-ray Baggage Screening with Deep Convolutional Object Detection
Kevin J Liang, John B. Sigman, Gregory P. Spell +5
For the safety of the traveling public, the Transportation Security Administration (TSA) operates security checkpoints at airports in the United States, seeking to keep dangerous i…
Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis
Andrew Stevens, Yunchen Pu, Yannan Sun +2
A multi-way factor analysis model is introduced for tensor-variate data of any order. Each data item is represented as a (sparse) sum of Kruskal decompositions, a Kruskal-factor an…