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Found 4 papers, 1 papers with code
Classifying Autism from Crowdsourced Semi-Structured Speech Recordings: A Machine Learning Approach
We train our classifiers on our novel dataset of cellphone-recorded child speech audio curated from Stanford's Guess What?
MULTI-LEVEL APPROACH TO ACCURATE AND SCALABLE HYPERGRAPH EMBEDDING
Many problems such as node classification and link prediction in network data can be solved using graph embeddings, and a number of algorithms are known for constructing such embeddings.
Classification of Abnormal Hand Movement for Aiding in Autism Detection: Machine Learning Study
This work aims to demonstrate the feasibility of deep learning technologies for detecting hand flapping from unstructured home videos as a first step towards validating whether models and digital technologies can be leveraged to aid with autism diagnoses.
Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels
While the F1-score for a one-hot encoded classifier is much higher (94. 33% vs. 78. 68%) with respect to the ground truth CAFE labels, the output probability vector of the crowd-trained classifier more closely resembles the distribution of human labels (t=3. 2827, p=0. 0014).
