Transformers have achieved great success in many artificial intelligence fields, such as
natural language processing, computer vision, and audio processing. Therefore, it is natural …

Transformer is a powerful model for text understanding. However, it is inefficient due to its
quadratic complexity to input sequence length. Although there are many methods on …

Pre-trained transformer models, such as BERT and T5, have shown to be highly effective at
ad hoc passage and document ranking. Due to the inherent sequence length limits of these …

Paralinguistic speech processing is important in addressing many issues, such as sentiment
and neurocognitive disorder analyses. Recently, Transformer has achieved remarkable …

Elucidating and accurately predicting the druggability and bioactivities of molecules plays a
pivotal role in drug design and discovery and remains an open challenge. Recently, graph …

Abstract The adoption of Deep Neural Networks (DNNs) has greatly benefited Natural
Language Processing (NLP) during the past decade. However, the demands of long …

The existing approaches to image captioning tend to adopt Transformer-based architectures
with grid features, which represent the state-of-the-art. However, the strategies are prone to …

With the surge in mobile gaming, accurately predicting user spending on newly downloaded
games has become paramount for maximizing revenue. However, the inherently …

In the context of long document classification (LDC), effectively utilizing multi-modal
information encompassing texts and images within these documents has not received …

Abstract Interpretation of non-coding genome remains an unsolved challenge in human
genetics due to impracticality of exhaustively annotating biochemically active elements in all …