14 citations · 18 across the 6 of their papers we have counts for
6 papers
Low-Frequency Shortcuts in Texture-Driven Visual Learning
Utku Şirin, Cathy Hou, David Alvarez-Melis +2
Neural networks suffer from shortcut learning, where learned features generalize well to the training set but not to in-distribution (ID) or out-of-distribution (OOD) test sets. Ex…
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…
Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging
Peter Washington, Aaron Kline, Onur Cezmi Mutlu +6
Activity recognition computer vision algorithms can be used to detect the presence of autism-related behaviors, including what are termed "restricted and repetitive behaviors", or…
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…