An NYU team uses machine learning to analyze neural activity data and uncover how speech is produced. In a recent paper ...

The encoder employs stacked self-attention layers to capture spatial-temporal dependencies and thematic continuity, while the decoder leverages a two-stage attention mechanism combining multi-head ...

The existing deep learning based reversible data hiding (RDH) predictors typically adopt standard convolutions for extracting features, which inherently fails to capture contextual information across ...