Language-Induced Priors from LLMs guide source selection in cold-start domain adaptation through an EM algorithm, matching oracle MSE under a correct prior and remaining asymptotically consistent.
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
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Transfer learning from capture-recapture data improves temporal abundance and trend estimates from catch-per-unit-effort data by accounting for variable detection probabilities.
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Language-Induced Priors for Domain Adaptation
Language-Induced Priors from LLMs guide source selection in cold-start domain adaptation through an EM algorithm, matching oracle MSE under a correct prior and remaining asymptotically consistent.
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Accounting for variable detection functions in temporal abundance modeling via transfer learning
Transfer learning from capture-recapture data improves temporal abundance and trend estimates from catch-per-unit-effort data by accounting for variable detection probabilities.