Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …

As ChatGPT goes viral, generative AI (AIGC, aka AI-generated content) has made headlines
everywhere because of its ability to analyze and create text, images, and beyond. With such …

Abstract Machine learning models are increasingly used in materials studies because of
their exceptional accuracy. However, the most accurate machine learning models are …

In the summarization domain, a key requirement for summaries is to be factually consistent
with the input document. Previous work has found that natural language inference (NLI) …

Given the rapid development of brain–computer interfaces, emotion identification based on
EEG signals has emerged as a new study area with tremendous importance in recent years …

Position encoding has recently been shown to be effective in transformer architecture. It
enables valuable supervision for dependency modeling between elements at different …

Data augmentation, the artificial creation of training data for machine learning by
transformations, is a widely studied research field across machine learning disciplines …

Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …

Self-attention has the promise of improving computer vision systems due to parameter-
independent scaling of receptive fields and content-dependent interactions, in contrast to …

A long time ago in the machine learning literature, the idea of incorporating a mechanism
inspired by the human visual system into neural networks was introduced. This idea is …