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Satellite imagery unlocks fine-grained socioeconomic predictions with calibrated uncertainty, even without direct ground truth.
Transferring latent group priors across cohorts eliminates the standard bandit cold-start penalty while actively preventing the premature catalog exhaustion that plagues real-world deployments.
Event-pattern complexity can drastically affect model performance, with some neural STPPs collapsing under pressure while others remain robust.
Contextual embeddings can dramatically enhance predictive accuracy in spatio-temporal forecasting, especially when historical data is sparse.
HawkesNest reveals that even structurally aligned models can falter dramatically under complex spatiotemporal conditions, exposing critical vulnerabilities in STPP evaluations.
Brain tumor segmentation gets a boost in both accuracy and interpretability with SegGuidedNet, a lightweight architecture that rivals ensembles without needing post-hoc explainability.
