Baylor University researchers are studying deep learning, with a focus on improving medical imaging and advancing the future of truly smart houses that will perform all manual labor for occupants.
The research is divided into two categories: distributed deep learning and energy-efficient deep learning. Distributed deep learning involves investigating how to use several local machines to compute different parts of the main neural network, while energy-efficient deep learning focuses on the problem of being able to provide a constant source of energy for continuous projects.
The researchers are using deep learning to analyze medical images, including positron emission tomography (PET) scans and computed tomography (CT) scans.
The team is also leading a smart home project to determine whether a house can measure a person's overall health; sensors throughout the house would read a person’s biorhythms and send alerts to the home's occupants if needed.
From Baylor Lariat (TX)
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