M³ partitions space by physical variation using multi-scale Morton ordering to balance training measures, yielding up to 4.7× lower error on industrial volumetric datasets and outperforming higher-resolution training even after aggressive subsampling.
A short survey on importance weighting for machine learning
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Meta-learning with in-context control samples closes the domain gap for mechanism-of-action classification, raising accuracy on new batches from 0.862 to 0.935 on the JUMP-CP dataset.
The paper introduces two general frameworks for conditional two-sample testing by converting conditional independence tests or using density ratio estimation to enable marginal comparisons.
citing papers explorer
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M$^3$: Reframing Training Measures for Discretized Physical Simulations
M³ partitions space by physical variation using multi-scale Morton ordering to balance training measures, yielding up to 4.7× lower error on industrial volumetric datasets and outperforming higher-resolution training even after aggressive subsampling.
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Closing the Domain Gap in Biomedical Imaging by In-Context Control Samples
Meta-learning with in-context control samples closes the domain gap for mechanism-of-action classification, raising accuracy on new batches from 0.862 to 0.935 on the JUMP-CP dataset.
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General Frameworks for Conditional Two-Sample Testing
The paper introduces two general frameworks for conditional two-sample testing by converting conditional independence tests or using density ratio estimation to enable marginal comparisons.