Episodic sampling for class-balanced batches in low-data CT segmentation delays overfitting compared to random or weighted sampling, revealing training iteration budget as a key evaluation confound.
Not all samples are created equal: Deep learning with importance sampling
2 Pith papers cite this work. Polarity classification is still indexing.
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A greedy submodular maximization method for mini-batch selection in DNN training yields better generalization than SGD on standard datasets.
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Disentangling Sampling from Training Budget in Class-Imbalanced CT Body Composition Segmentation
Episodic sampling for class-balanced batches in low-data CT segmentation delays overfitting compared to random or weighted sampling, revealing training iteration budget as a key evaluation confound.
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Submodular Batch Selection for Training Deep Neural Networks
A greedy submodular maximization method for mini-batch selection in DNN training yields better generalization than SGD on standard datasets.