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

In recent years, deep learning has evolved as a rapidly growing and stimulating field of
machine learning and has redefined state-of-the-art performances in a variety of …

Recent vision transformers, large-kernel CNNs and MLPs have attained remarkable
successes in broad vision tasks thanks to their effective information fusion in the global …

Deep learning has become a predominant method for solving data analysis problems in
virtually all fields of science and engineering. The increasing complexity and the large …

Long-term time series forecasting (LTSF) provides substantial benefits for numerous real-
world applications, whereas places essential demands on the model capacity to capture …

An increasing demand for model explainability has accompanied the widespread adoption
of transformers in various fields of applications. In this paper, we conduct a survey of the …

Over the years, network traffic analysis and generation have advanced significantly. From
traditional statistical methods, the field has progressed to sophisticated deep learning …

Accurately recognizing video-based smoke is still a profoundly challenging task due to the
special characteristics of smoke, such as non-rigid morphology, semi-transparent …

Long sequence time-series forecasting (LSTF) is a significant and challenging task. Many
real-world applications require long-term forecasting of time series. In recent years …

In conversational recommender systems (CRSs), conversations usually involve a set of
items and item-related entities or attributes, eg, director is a related entity of a movie. These …