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Explaining latent representations of generative models with large multimodal models
Learning interpretable representations of data generative latent factors is an important topic for the development of artificial intelligence.
SurroCBM: Concept Bottleneck Surrogate Models for Generative Post-hoc Explanation
Explainable AI seeks to bring light to the decision-making processes of black-box models.
Large Language Models for Spatial Trajectory Patterns Mining
Identifying anomalous human spatial trajectory patterns can indicate dynamic changes in mobility behavior with applications in domains like infectious disease monitoring and elderly care.
Graph Neural Network for spatiotemporal data: methods and applications
Graph neural networks (GNNs) have emerged as a powerful tool for modeling and understanding data with dependencies to each other such as spatial and temporal dependencies.
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