3 accepted papers
Large language models (LLMs) can understand human instructions, showing their potential for pragmatic applications beyond traditional NLP tasks. However, they still struggle with complex instructions, which can be either complex task descriptions that require multiple tasks and constraints, or compl…
Self-supervised learning has been gaining attention in the field of sleep stage classification. It learns representations with unlabeled electroencephalography (EEG) signals, which alleviates the cost of labeling for specialists. However, most self-supervised approaches assume only the two augmented…
The lack of well-annotated datasets in computational pathology (CPath) obstructs the application of deep learning techniques for classifying medical images. Many CPath workflows involve transferring learned knowledge between various image domains through transfer learning. Currently, most transfer l…