Tag: self-attention

A hybrid CNN-BiLSTM model with multi-head self-attention classifies five EEG brain states, including seizures, with 98.83 percent accuracy on a ...

A new review maps how Transformer architectures, from temporal and spatial attention models to self-supervised brain foundation models, are reshaping ...

A compact deep learning model that mathematically separates normal machining signals from anomalous disturbances has set new benchmarks for predicting ...

Researchers have built a transformer that treats missing Wi-Fi signals as informative data, achieving the lowest errors among reproduced baselines ...

A hybrid Transformer-BiLSTM deep learning framework with a dual nature-inspired feature selection method reports up to 99 percent accuracy in ...

A new hybrid framework called LLMTransRec combines transformer sequence modeling with large language model embeddings to improve recommendations across sparse ...

Researchers have developed a chronology-aware variational autoencoder that generates faithful Chinese black-and-white traditional motifs from a dataset of only 300 ...

Researchers have developed an interpretable deep reinforcement learning framework that schedules wind turbine maintenance to maximize power generation and minimize ...

A new multimodal deep learning framework fuses Log-Mel spectrograms and BERT embeddings through self-attention to automatically score spoken English fluency ...

Researchers have built a transformer-based intrusion detection framework that models relationships among network traffic features and outperforms nine baseline AI ...