Animal vocalisations and natural soundscapes are fascinating objects of study, and contain
valuable evidence about animal behaviours, populations and ecosystems. They are studied …

Temporal point processes (TPP) are probabilistic generative models for continuous-time
event sequences. Neural TPPs combine the fundamental ideas from point process literature …

The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …

Continuous-time dynamic graphs naturally abstract many real-world systems, such as social
and transactional networks. While the research on continuous-time dynamic graph …

Modeling continuous-time dynamics on irregular time series is critical to account for data
evolution and correlations that occur continuously. Traditional methods including recurrent …

Neural ordinary differential equations describe how values change in time. This is the
reason why they gained importance in modeling sequential data, especially when the …

Autoregressive neural networks within the temporal point process (TPP) framework have
become the standard for modeling continuous-time event data. Even though these models …

Human daily activities, such as working, eating out, and traveling, play an essential role in
contact tracing and modeling the diffusion patterns of the COVID-19 pandemic. However …

Recent years have witnessed the rise of misinformation campaigns that spread specific
narratives on social media to manipulate public opinions on different areas, such as politics …

Traffic congestion event prediction is an important yet challenging task in intelligent
transportation systems. Many existing works about traffic prediction integrate various …