44 citations · 60 across the 12 of their papers we have counts for
8 papers · 1 filter
Motion correction in MRI using deep learning and a novel hybrid loss function
Lei Zhang, Xiaoke Wang, Michael Rawson +6
Purpose To develop and evaluate a deep learning-based method (MC-Net) to suppress motion artifacts in brain magnetic resonance imaging (MRI). Methods MC-Net was derived from a UNet…
Petri Nets for Concurrent Programming
Marshall Rawson, Michael Rawson
Concurrent programming is used in all large and complex computer systems. However, concurrency errors and system failures (ex: crashes and deadlocks) are common. We find that Petri…
Renormalized Sparse Neural Network Pruning
Michael G. Rawson
Large neural networks are heavily over-parameterized. This is done because it improves training to optimality. However once the network is trained, this means many parameters can b…
Linear Run Time of Persistent Homology Computation with GPU Parallelization
Michael G. Rawson
Persistent homology is a crucial invariant that is used in many areas to understand data. The run time is a hindrance to its use on most large datasets. We give a parallel…
Topological Data Analysis for Word Sense Disambiguation
Michael Rawson, Samuel Dooley, Mithun Bharadwaj +1
We develop and test a novel unsupervised algorithm for word sense induction and disambiguation which uses topological data analysis. Typical approaches to the problem involve clust…
Optimal Transport for Super Resolution Applied to Astronomy Imaging
Michael Rawson, Jakob Hultgren
Super resolution is an essential tool in optics, especially on interstellar scales, due to physical laws restricting possible imaging resolution. We propose using optimal transport…
Top-K Ranking Deep Contextual Bandits for Information Selection Systems
Jade Freeman, Michael Rawson
In today's technology environment, information is abundant, dynamic, and heterogeneous in nature. Automated filtering and prioritization of information is based on the distinction…
Convergence Guarantees for Deep Epsilon Greedy Policy Learning
Michael Rawson, Radu Balan
Policy learning is a quickly growing area. As robotics and computers control day-to-day life, their error rate needs to be minimized and controlled. There are many policy learning…