no code implementations • 11 Sep 2018 • Michael J. Anderson, Jonathan I. Tamir, Javier S. Turek, Marcus T. Alley, Theodore L. Willke, Shreyas S. Vasanawala, Michael Lustig
Our improvements to the pipeline on a single machine provide a 3x overall reconstruction speedup, which allowed us to add algorithmic changes improving image quality.
no code implementations • 16 Aug 2016 • Michael J. Anderson, Mihai Capotă, Javier S. Turek, Xia Zhu, Theodore L. Willke, Yida Wang, Po-Hsuan Chen, Jeremy R. Manning, Peter J. Ramadge, Kenneth A. Norman
The scale of functional magnetic resonance image data is rapidly increasing as large multi-subject datasets are becoming widely available and high-resolution scanners are adopted.
1 code implementation • 21 Nov 2015 • Shihao Ji, S. V. N. Vishwanathan, Nadathur Satish, Michael J. Anderson, Pradeep Dubey
One way to understand BlackOut is to view it as an extension of the DropOut strategy to the output layer, wherein we use a discriminative training loss and a weighted sampling scheme.