4 papers
Seahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modeling
Yahya Aalaila, Gerrit GroÃmann, Sebastian Vollmer
Spatiotemporal point processes (STPPs) model event data in continuous time and space, with applications in mobility, epidemiology, and public safety. Recent neural STPPs span expre…
HawkesNest: A Multi-Axis Synthetic Benchmark for Spatiotemporal Pattern Complexity
Yahya Aalaila, Sumantrak Mukherjee, Gerrit GroÃmann +1
Evaluation of spatiotemporal point process (STPP) models relies heavily on opaque real-world datasets, where latent generative structure is unknown and model failures are difficult…
Guiding Exploration in Reinforcement Learning Through LLM-Augmented Observations
Vaibhav Jain, Gerrit Grossmann
Reinforcement Learning (RL) agents often struggle in sparse-reward environments where traditional exploration strategies fail to discover effective action sequences. Large Language…
Auto-encoding Molecules: Graph-Matching Capabilities Matter
Magnus Cunow, Gerrit GroÃmann
Autoencoders are effective deep learning models that can function as generative models and learn latent representations for downstream tasks. The use of graph autoencoders - with b…