Recent advances in location-acquisition techniques have generated massive spatial
trajectory data. Recurrent Neural Networks (RNNs) are modern tools for modeling such …

Multi-agent trajectory prediction is crucial for various practical applications, spurring the
construction of many large-scale trajectory datasets, including vehicles and pedestrians …

The objective of pedestrian trajectory prediction is to estimate the future paths of pedestrians
by leveraging historical observations, which plays a vital role in ensuring the safety of self …

Trajectory computing is a pivotal domain encompassing trajectory data management and
mining, garnering widespread attention due to its crucial role in various practical …

Training dynamic models, such as neural ODEs, on long trajectories is a hard problem that
requires using various tricks, such as trajectory splitting, to make model training work in …

Trajectory data combines the complexities of time series, spatial data, and (sometimes
irrational) movement behavior. As data availability and computing power have increased, so …

Recurrent neural networks (RNNs) with continuous-time hidden states are a natural fit for
modeling irregularly-sampled time series. These models, however, face difficulties when the …

Time series with non-uniform intervals occur in many applications, and are difficult to model
using standard recurrent neural networks (RNNs). We generalize RNNs to have continuous …

Spatiotemporal predictive learning, which predicts future frames through historical prior
knowledge with the aid of deep learning, is widely used in many fields. Previous work …

Neural Ordinary Differential Equations (NODEs) use a neural network to model the
instantaneous rate of change in the state of a system. However, despite their apparent …