Peter Sutor is a Ph.D. student at the University of Maryland in the Department of Computer Science. His work focuses on vector encoding. He currenty works as an engineer at Simuli. He has published over 14 publications in high-dimensional computing and served as conference chair for the Hyperdimensional Computing Conference (HDC).
Talk title and abstract:
Efficient and Robust Vision Learning with Hyperdimensional Computing
Hyperdimensional computing allows low memory encoding of data that can be efficiently computed in hypergeometric spaces. These spaces afford few-shot learning techniques without suffering accuracy when compared to traditional deep learning methods. We'll show a new method of AI for classification that is cheaper to train and use.
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