Tag: attention mechanisms

Researchers have introduced mosGraphFlow, an integrative graph neural network framework that combines multi-omic molecular layers and biological regulatory pathways to ...

A multi-metric quantitative evaluation of four multiple instance learning models on thyroid fine-needle aspiration smear images revealed that high-performance attention ...

Researchers have introduced ALDA4Rec, an adaptive recommendation model that combines graph denoising, multi-scale embeddings, and dynamic weighting to improve sequential ...

Researchers developed a glowworm swarm-optimized variational autoencoder that combines visual features and structured spatial data to distinguish authentic interior scenes ...

An attention-enhanced ResNet-50 deep learning model achieved an accuracy of 0.833 and an area under the curve of 0.855 when ...

Researchers have introduced DC-MISR, a session-based recommendation framework that adaptively models multi-intent dynamics using global relational priors to enhance next-item ...

A systematic survey evaluates modern deep learning architectures and benchmark datasets for traffic volume prediction across recent transport studies.

Researchers have introduced AMST, an attention-guided spatio-temporal masking framework that enhances self-supervised skeleton action recognition across multiple benchmark datasets.

A hierarchical attention-based reinforcement learning framework improved slice admission rates by up to 20 percent and cut resource overhead by ...

A physics-guided deep learning model trained on five years of monitoring data from the 1,650-metre Xihoumen Bridge predicts wind-induced buffeting ...