Background: Few-shot learning (FSL) is a class of machine learning methods that require
small numbers of labeled instances for training. With many medical topics having limited …

Objective: Few-shot learning (FSL) methods require small numbers of labeled instances for
training. As many medical topics have limited annotated textual data in practical settings …

Objective: The present study aims to investigate changes in the frequency of parent-child
contact among Europeans aged 65 years and over within the context of the COVID-19 …

Deep learning becomes an elevated context regarding disposing of many machine learning
tasks and has shown a breakthrough upliftment to extract features from unstructured data …

Recent research on robust representations of biomedical names has focused on modeling
large amounts of fine-grained conceptual distinctions using complex neural encoders. In this …

Objective Few-shot learning (FSL) methods require small numbers of labeled instances for
training. As many medical topics have limited annotated textual data in practical settings …

Pre-trained distributional representations of words and phrases have become omnipresent
in natural language processing (NLP), where they have led to significant improvements in …