Tag: self-attention

A tutorial review in Frontiers in Digital Health analyzes the architecture, clinical applications, and critical safety limitations of large language ...

A systematic review of 163 studies traces the progression of vision transformers and highlights where convolutional networks remain competitive.

A new AI framework called DSE-TAM jointly models spatial relationships among EEG electrodes and temporal dynamics of brain activity, improving ...

A new study reports a quantum-inspired recommendation system combining fuzzy clustering, generative adversarial networks, and a dandelion-based optimizer that achieved ...

A deep feature extraction framework enhances traditional classifiers to predict long-term loan delinquency transitions using tabular credit data with short ...

Researchers have shown that randomly shuffling embedded patch tokens inside vision transformer surrogate models during adversarial example generation substantially improves ...

A multi-scale vision transformer analyzing UAV hyperspectral images identified pine wilt disease at the green attack and early stages with ...

A four-stage hybrid framework combining convolutional spectral compression, a Transformer encoder, and U-Net spatial extraction reports 99.85% overall accuracy on ...

A new lightweight model combines self-attention-enhanced YOLOv5 with 2D image sonar to detect and localize small underwater targets at long ...

A music-aware transformer framework combining multi-scale spectrogram learning and adaptive attention optimization reported accuracies of 93.5%, 99.2%, and 97.1% on ...