55 citations · 76 across the 7 of their papers we have counts for
7 papers
Identifying Sparsely Active Circuits Through Local Loss Landscape Decomposition
Brianna Chrisman, Lucius Bushnaq, Lee Sharkey
Much of mechanistic interpretability has focused on understanding the activation spaces of large neural networks. However, activation space-based approaches reveal little about the…
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…
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images
Peter Washington, Cezmi Onur Mutlu, Aaron Kline +7
Computer Vision (CV) classifiers which distinguish and detect nonverbal social human behavior and mental state can aid digital diagnostics and therapeutics for psychiatry and the b…
Classification of Abnormal Hand Movement for Aiding in Autism Detection: Machine Learning Study
Anish Lakkapragada, Aaron Kline, Onur Cezmi Mutlu +5
A formal autism diagnosis can be an inefficient and lengthy process. Families may wait months or longer before receiving a diagnosis for their child despite evidence that earlier i…
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…