no code implementations • 31 Jan 2023 • Simiao Ren, Yang Deng, Willie J. Padilla, Leslie Collins, Jordan Malof
Deep learning (DL) is revolutionizing the scientific computing community.
no code implementations • 23 Oct 2022 • Francesco Luzi, Aneesh Gupta, Leslie Collins, Kyle Bradbury, Jordan Malof
In this paper we systematically compare the impact of adding transformer structures into state-of-the-art segmentation models for overhead imagery.
no code implementations • 29 Jan 2022 • Simiao Ren, Yang Deng, Willie J. Padilla, Jordan Malof
Deep learning (DL) is revolutionizing the scientific computing community.
1 code implementation • 14 Jan 2022 • Simiao Ren, Jordan Malof, T. Robert Fetter, Robert Beach, Jay Rineer, Kyle Bradbury
In this work, we explore the viability and cost-performance tradeoff of using automatic SHS detection on unmanned aerial vehicle (UAV) imagery as an alternative to satellite imagery.
no code implementations • ICLR 2022 • Juncheng Dong, Simiao Ren, Yang Deng, Omar Khatib, Jordan Malof, Mohammadreza Soltani, Willie Padilla, Vahid Tarokh
To this end, we propose a physics-infused deep neural network based on the Blaschke products for phase retrieval.
1 code implementation • NeurIPS 2021 • Yang Deng*, Juncheng Dong*, Simiao Ren*, Omar Khatib, Mohammadreza Soltani, Vahid Tarokh, Willie Padilla, Jordan Malof
Recently, it has been shown that deep learning can be an alternative solution to infer the relationship between an AEM geometry and its properties using a (relatively) small pool of CEMS data.
no code implementations • 16 Jan 2021 • Bohao Huang, Jichen Yang, Artem Streltsov, Kyle Bradbury, Leslie M. Collins, Jordan Malof
Energy system information valuable for electricity access planning such as the locations and connectivity of electricity transmission and distribution towers, termed the power grid, is often incomplete, outdated, or altogether unavailable.
1 code implementation • NeurIPS 2020 • Simiao Ren, Willie Padilla, Jordan Malof
We consider the task of solving generic inverse problems, where one wishes to determine the hidden parameters of a natural system that will give rise to a particular set of measurements.