30 citations · 42 across the 5 of their papers we have counts for
5 papers
Computer-aided abnormality detection in chest radiographs in a clinical setting via domain-adaptation
Abhishek K Dubey, Michael T Young, Christopher Stanley +2
Deep learning (DL) models are being deployed at medical centers to aid radiologists for diagnosis of lung conditions from chest radiographs. Such models are often trained on a larg…
Towards the Development of Entropy-Based Anomaly Detection in an Astrophysics Simulation
Drew Schmidt, Bronson Messer, M. Todd Young +1
The use of AI and ML for scientific applications is currently a very exciting and dynamic field. Much of this excitement for HPC has focused on ML applications whose analysis and c…
Challenges in Markov chain Monte Carlo for Bayesian neural networks
Theodore Papamarkou, Jacob Hinkle, M. Todd Young +1
Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in Bayesian neural networks (BNNs). This paper initially reviews the main challenges in sampling from the para…
Exascale Deep Learning for Scientific Inverse Problems
Nouamane Laanait, Joshua Romero, Junqi Yin +6
We introduce novel communication strategies in synchronous distributed Deep Learning consisting of decentralized gradient reduction orchestration and computational graph-aware grou…
Deep Generative Model Driven Protein Folding Simulation
Heng Ma, Debsindhu Bhowmik, Hyungro Lee +4
Significant progress in computer hardware and software have enabled molecular dynamics (MD) simulations to model complex biological phenomena such as protein folding. However, enab…