How Americans are using AI - Gallup

Gallup poll shows AI use surging at work across industries—boosting productivity but raising risks for vulnerable roles.
Frontiers

Exploring physical and functional EEG connectivity with multilayer graph transformer convolutional networks for emotion recognition

1 School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, China 2 School of Engineering, University of Birmingham, Birmingham, United Kingdom Electroencephalogram ...
IEEE

MotifGT-DTI: Pivotal Motif-Based Graph Transformer Model Improves Drug–Target Interaction Prediction

Abstract: Predicting drug–target interactions (DTIs) plays an essential role in drug discovery and drug repurposing. Although significant performance improvements have been achieved in DTI prediction, ...
IEEE

GateSleepNet: A Dual-Level Spatiotemporal Graph-Transformer Architecture for Automatic Sleep Staging

Abstract: Automatic sleep staging is critical for understanding sleep patterns and diagnosing sleep-related disorders, yet traditional manual scoring methods remain time-consuming, labor-intensive, ...
Frontiers

Multi-perspective hotel operation process anomaly prediction method based on graph transformer and autoencoder

Due to the complexity of hotel operation processes, abnormal situations are inevitable, making proactive anomaly prediction essential for ensuring operational stability. Although current deep learning ...
C&EN

Molecular Property Prediction Based on Improved Graph Transformer Network and Multitask Joint Learning Strategy

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...
GitHub

AutoGraph: Transformers are Scalable Graph Generators

The flattening process relies on sampling a sequence of random trail segments with neighborhood information (i.e. a SENT), by traversing the graph through a strategy similar to depth-first search.
unite

What Every Data Scientist Should Know About Graph Transformers and Their Impact on Structured Data

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...