32 citations · 60 across the 5 of their papers we have counts for
5 papers
GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction
Batu Ozturkler, Arda Sahiner, Tolga Ergen +6
Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction. These networks unroll iterative optimization algorithms by alternating between phys…
Using a Novel COVID-19 Calculator to Measure Positive U.S. Socio-Economic Impact of a COVID-19 Pre-Screening Solution (AI/ML)
Richard Swartzbaugh, Amil Khanzada, Praveen Govindan +8
The COVID-19 pandemic has been a scourge upon humanity, claiming the lives of more than 5.1 million people worldwide; the global economy contracted by 3.5% in 2020. This paper pres…
Using Deep Learning with Large Aggregated Datasets for COVID-19 Classification from Cough
Esin Darici Haritaoglu, Nicholas Rasmussen, Daniel C. H. Tan +12
The Covid-19 pandemic has been one of the most devastating events in recent history, claiming the lives of more than 5 million people worldwide. Even with the worldwide distributio…
Randomized sketches for kernels: Fast and optimal non-parametric regression
Yun Yang, Mert Pilanci, Martin J. Wainwright
Kernel ridge regression (KRR) is a standard method for performing non-parametric regression over reproducing kernel Hilbert spaces. Given samples, the time and space complexity…
Iterative Hessian sketch: Fast and accurate solution approximation for constrained least-squares
Mert Pilanci, Martin J. Wainwright
We study randomized sketching methods for approximately solving least-squares problem with a general convex constraint. The quality of a least-squares approximation can be assessed…