In the realm of time series forecasting (TSF), it is imperative for models to adeptly discern
and distill hidden patterns within historical time series data to forecast future states …

As one of the most representative DL techniques, Transformer architecture has empowered
numerous advanced models, especially the large language models (LLMs) that comprise …

In the post-deep learning era, the Transformer architecture has demonstrated its powerful
performance across pre-trained big models and various downstream tasks. However, the …

With the fast improvement of machine learning, reinforcement learning (RL) has been used
to automate human tasks in different areas. However, training such agents is difficult and …

Images corrupted by rain streaks often lose vital frequency information for perception, and
image deraining aims to solve this problem, which relies on global and local degradation …

Images corrupted by rain streaks often lose vital frequency information for perception, and
image deraining aims to solve this issue which relies on global and local degradation …

While tactile sensing is widely accepted as an important and useful sensing modality, its use
pales in comparison to other sensory modalities like vision and proprioception. AnySkin …

The battery systems of electric vehicles (EVs) are directly impacted by battery temperature in
terms of thermal runaway and failure. However, uncertainty about thermal runaway, dynamic …

Recent advancements in imitation learning have been largely fueled by the integration of
sequence models, which provide a structured flow of information to effectively mimic task …