sparse-data

Presents SceneAligner, a deep learning approach for floorplan localization that uses 3D scene reconstruction and cross-modal correspondence learning to work in real-world environments with limited data.

This paper introduces SPADE, a novel algorithm for drug discovery that efficiently identifies high-quality ligands from sparse data using only ~40 tests. It demonstrates superior sample efficiency and speed compared to deep learning and Bayesian optimization methods.

Academic study compares SARIMAX and Poisson regression for forecasting sparse, bursty vulnerability-sighting time-series, finding count-based models more stable.