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The Ohio State University, Max-Planck-Institut f眉r Astronomie
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Bi-CFM achieves over two orders of magnitude speedup while improving accuracy in solving inverse problems of chaotic systems, even under conservation constraints.
Simple MLPs pre-trained on low-resolution spectra can generalize surprisingly well to higher-resolution data for stellar parameter estimation, sometimes outperforming transformer-based embeddings.
Historical astronomical literature contains predictive structure for future concept-object associations that is missed by both recency heuristics and neighborhood-based methods.
