Multi-agent trajectory forecasting has recently attracted a lot of attention due to its
widespread applications including autonomous driving. Most previous methods use RNNs …

This paper introduces a new learning algorithm for accurate, physically driven time series
prediction. The fundamental assumption behind the method is that the phenomena follow …

Predicting future action locations is vital for applications like human-robot collaboration.
While some computer vision tasks have made progress in predicting human actions …

Transformers have been an efficient alternative to recurrent neural networks in many
sequential learning tasks. When adapting transformers to modeling trajectories, we …

In fluid physics, data-driven models to enhance or accelerate time to solution are becoming
increasingly popular for many application domains, such as alternatives to turbulence …

Daily activity data that records individuals' various types of activities in daily life are widely
used in many applications such as activity scheduling, activity recommendation, and …

The great learning ability of deep learning models facilitates us to comprehend the real
physical world, making learning to simulate complicated particle systems a promising …

Stock selection attempts to rank a list of stocks for optimizing investment decision making,
aiming at minimizing investment risks while maximizing profit returns. Recently, researchers …

Electroencephalography (EEG) is a major tool for studying neurophysiological processes.
Investigating reliable representations from highly noisy measurements is a pending …

The ubiquitous GPS-enabled devices (eg, vehicles and mobile phones) have led to the
unexpected growth in trajectory data that can be well utilized for intelligent city management …