1 citations · 1 across the 1 of their papers we have counts for
3 papers
cs.LG2022★ 1 cited
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…
cs.CV2021
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…
cs.LG2019
Data-driven simulation for general purpose multibody dynamics using deep neural networks
Hee-Sun Choi, Junmo An, Jin-Gyun Kim +5
In this paper, a machine learning-based simulation framework of general-purpose multibody dynamics is introduced. The aim of the framework is to generate a well-trained meta-model…