Princeton students take a deep dive into deep learning computing | Campus Life - Princeton University

On Oct. 16-17, some 60 Princeton graduate students and postdocs — along with a handful of undergraduates — explored the most widely used deep learning techniques for computer vision tasks and delved into using new parallel computing programs to dramatically speed up applications, says Melissa Moss, Office of Communications at Princeton University.

Since an early flush of optimism in the 1950s, smaller subsets of artificial intelligence, first machine learning, then deep learning, have created ever larger disruptions.
Photo: courtesy of NVIDIA

The two daylong workshops were led by experts  from the NVIDIA Deep Learning Institute and were sponsored by the Princeton Institute for Computational Science and Engineering (PICSciE), part of the Princeton Research Computing consortium and co-sponsored by the Center for Statistics and Machine Learning.

Deep learning, perhaps the most rapidly growing branch of machine learning, loosely models itself after the complex and non-linear way the human brain analyzes information and makes predictions. Computer vision programmers realized that, by the age of 3, young children have absorbed information about hundreds of millions of objects they see in the world around them; it’s this vast and varied input of data that trains them to recognize the same object in very different contexts.

“We train an artificial neural network to recognize and classify things in a somewhat similar way,” said NVIDIA solutions architect Brad Palmer. “The algorithm gets better at identifying something the more it sees that object in varied situations.” (A Princeton professor, Kai Li, the Paul M. Wythes and Marcia R. Wythes Professor in Computer Science and an associated faculty with PICSciE, was on the team that created the world’s largest visual database, Imagenet, a crucial training resource for computer vision programmers.)

One attendee of the deep learning workshop was Princeton computer science doctoral student Ksenia Sokolova, who works on a research project with Olga Troyanskaya, professor of computer science and the Lewis-Sigler Institute for Integrative Genomics and an associated faculty with PICSciE. Sokolova said she attended the workshop because of this technology’s potential in genomics research and precision medicine. “I am currently working on a deep learning model that would help us get better insight into the dependencies between mutations in the DNA and diseases the person might have,” she said.
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Source: Princeton University