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Using Human Perception to Inform Machine Perception
March 28 @ 5:30 pm - 6:30 pm
Modern machine learning has origins in human learning, taking cues from human perception to build, train and evaluate machine learning models. As machine learning (ML) has begun to outperform humans in many challenging tasks, the focus has shifted from modeling humans to simply improving the performance of these ML models. We focus instead on what can be learned from human perception to improve these models and make them more transparent and understandable. With many applications of machine learning having real-world impacts on humans, we consider explainability essential for these models. In this talk, I will detail our approaches to explainable attribute recognition, prominent feature recognition, and face recognition. With each problem, we will highlight our influences from human perception. Speaker(s): Emily Hand, Ph.D., Virtual: https://events.vtools.ieee.org/m/352153