TabPack packs MLPs with diverse sampled hyperparameters into one vectorized model, selects ensemble members online during training, and matches tuned baselines at a fraction of the compute cost.
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A novel loss function enables effective segmentation training from summary statistics combined with minimal weak pixel supervision, outperforming statistics alone on medical imaging tasks.
citing papers explorer
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TabPack: Efficient Hyperparameter Ensembles for Tabular Deep Learning
TabPack packs MLPs with diverse sampled hyperparameters into one vectorized model, selects ensemble members online during training, and matches tuned baselines at a fraction of the compute cost.
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Learning to Segment using Summary Statistics and Weak Supervision
A novel loss function enables effective segmentation training from summary statistics combined with minimal weak pixel supervision, outperforming statistics alone on medical imaging tasks.