Neural scaling laws are invariant under bijective data transformations and change predictably with information resolution ρ under non-bijective transformations, enabling cross-domain transport of fitted exponents.
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Fine-tuning FinBERT on Finnish medical text produces embedding geometry shifts whose correlation with downstream performance the authors attempt to measure as a potential early signal for domain adaptation benefit.
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On the Invariance and Generality of Neural Scaling Laws
Neural scaling laws are invariant under bijective data transformations and change predictably with information resolution ρ under non-bijective transformations, enabling cross-domain transport of fitted exponents.
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Domain Fine-Tuning FinBERT on Finnish Histopathological Reports: Train-Time Signals and Downstream Correlations
Fine-tuning FinBERT on Finnish medical text produces embedding geometry shifts whose correlation with downstream performance the authors attempt to measure as a potential early signal for domain adaptation benefit.