14 citations · 18 across the 4 of their papers we have counts for
4 papers
An Exploration of Active Learning for Affective Digital Phenotyping
Peter Washington, Cezmi Mutlu, Aaron Kline +9
Some of the most severe bottlenecks preventing widespread development of machine learning models for human behavior include a dearth of labeled training data and difficulty of acqu…
Classifying Autism from Crowdsourced Semi-Structured Speech Recordings: A Machine Learning Approach
Nathan A. Chi, Peter Washington, Aaron Kline +5
Autism spectrum disorder (ASD) is a neurodevelopmental disorder which results in altered behavior, social development, and communication patterns. In past years, autism prevalence…
Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels
Peter Washington, Onur Cezmi Mutlu, Emilie Leblanc +8
Emotion classifiers traditionally predict discrete emotions. However, emotion expressions are often subjective, thus requiring a method to handle subjective labels. We explore the…
Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study
Peter Washington, Haik Kalantarian, John Kent +16
Background: Automated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However…