We held our next tinyML Talks webcast. Kristen Jaskie from Arizona State University has presented Positive Unlabeled Learning for Tiny ML on March 9, 2021.
Real world data is often only partially labeled. Because completely labeling data can be expensive or even impossible in some cases, a common scenario involves having only a small number of labeled samples from the class of interest, and a large quantity of unlabeled and unknown data. A classification boundary differentiating the underlying positive and negative classes is still desired. This is known as the Positive and Unlabeled learning problem, or PU learning, and is of growing importance in machine learning. Fortunately, PU learning algorithms exist that can create effective models using low power and memory requirements. In this talk, Ms. Jaskie will present several potential embedded applications for PU learning and describe how sensors, tiny ML, and PU learning all complement one another. In addition, she will describe low complexity solutions and explain why the techniques are so effective and in growing demand.
Kristen Jaskie is a Ph.D. student in Electrical Engineering in the ECEE school at ASU and she is a research associate with SenSIP. She received her B.S in Computer Science from the University of Washington and her M.S. in Computer Science specializing in AI and Machine Learning (ML) at the University of California San Diego. Kristen’s main areas of interest are in ML algorithm development and ML education. Specific interests include semi-supervised learning and the positive unlabeled learning problem. She is writing a monograph on the subject to be published later this year. In addition, Kristen owns her own consulting company and was a faculty member and department chair in Computer Science at Glendale Community College in Glendale, AZ for several years before returning to school to complete her Ph.D. She is expecting to graduate in Spring 2021.
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