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Found 2 papers, 1 papers with code
Spatio-Temporal Attention in Multi-Granular Brain Chronnectomes for Detection of Autism Spectrum Disorder
The traditional methods for detecting autism spectrum disorder (ASD) are expensive, subjective, and time-consuming, often taking years for a diagnosis, with many children growing well into adolescence and even adulthood before finally confirming the disorder.
SeDyT: A General Framework for Multi-Step Event Forecasting via Sequence Modeling on Dynamic Entity Embeddings
SeDyT consists of two components: a Temporal Graph Neural Network that generates dynamic entity embeddings in the past and a sequence model that predicts the entity embeddings in the future.
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