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Thursday, March 16 • 4:00pm - 5:30pm
Poster: Deep Learning Approaches to Structured Signal Recovery

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The promise of compressive sensing (CS) has been offset by two significant challenges. First,
real-world data is not exactly sparse in a fixed basis. Second, current high-performance recovery
algorithms are slow to converge, which limits CS to either non-real-time applications or scenarios
where massive back-end computing is available. We attack both of these challenges head-on by developing new signal recovery frameworks that learn the inverse transformation from observation vectors to original signals using deep learning techniques. When trained on a set of representative signals, these frameworks learn both a representation for the signals (addressing challenge one) and an inverse map approximating a greedy or convex recovery algorithm addressing challenge two). According to simulation results, our deep network frameworks closely approximate the solution produced by state-of-the-art CS recovery algorithms yet are thousands of times faster
in run time. The trade-off for the ultrafast run time is a computationally intensive, off-line training
procedure typical to deep networks. However, the training needs to be completed only once, which
makes the approach attractive for a host of sparse recovery applications. These applications are but not limited to magnetic resonance imaging (MRI), computed tomography (CT), coded-aperture imaging, and seismic data collection in oil and gas industry.


Thursday March 16, 2017 4:00pm - 5:30pm
Exhibit Hall BRC