29 citations · 76 across the 21 of their papers we have counts for
8 papers · 1 filter
A Comparison of Data Augmentation Methods for Training Deep Neural Networks on Synthetic Aperture Sonar
C. J. Moore, Gregory D. Vetaw, Jordan Malof
In this work we study Automatic Target Recognition (ATR) for Synthetic Aperture Sonar (SAS) data with a focus on deep neural networks (DNNs). The main challenge in training DNNs fo…
Closing Gaps in Emissions Monitoring with Climate TRACE
Brittany V. Lancellotti, Jordan M. Malof, Aaron Davitt +32
Global greenhouse gas emissions estimates are essential for monitoring and mitigation planning. Existing emissions datasets provide critical foundations for understanding emissions…
Does Deep Active Learning Work in the Wild?
Simiao Ren, Saad Lahrichi, Yang Deng +3
Deep active learning (DAL) methods have shown significant improvements in sample efficiency compared to simple random sampling. While these studies are valuable, they nearly always…
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…
Towards Robust Deep Active Learning for Scientific Computing
Simiao Ren, Yang Deng, Willie J. Padilla +1
Deep learning (DL) is revolutionizing the scientific computing community. To reduce the data gap, active learning has been identified as a promising solution for DL in the scientif…
Inverse deep learning methods and benchmarks for artificial electromagnetic material design
Simiao Ren, Ashwin Mahendra, Omar Khatib +3
Deep learning (DL) inverse techniques have increased the speed of artificial electromagnetic material (AEM) design and improved the quality of resulting devices. Many DL inverse te…