Buy-at-Bulk Facility Location on Trees
研究了树形实例上的买批量设施定位问题，提出针对单位需求和可分割变体的PTAS，并为电缆不可分割需求提供了近似算法。
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Learning Faster without Deeper Networks: A*-Inspired Batch Selection for Efficient CNN Training
Traditional CNN training relies on random mini-batch sampling, which often leads to rapid saturation of learning signals as most samples quickly become “easy,” thereby slowing convergence. This work proposes A*-inspired Batch Selection (A*-BS), the first approach to integrate A* search into batch selection, introducing a dynamic scoring mechanism that jointly considers sample loss difficulty and reuse penalties to adaptively select informative and diverse batches. Without modifying network architecture or optimizer, A*-BS achieves superior performance on lightweight CNNs across half of the MedMNIST-v2 benchmark tasks, outperforming ResNet-18/50 in both accuracy and AUC—by up to 15% relatively—while significantly accelerating training. These results demonstrate that intelligent batch sequencing can partially substitute for model depth.
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Buy-at-Bulk Facility Location on Trees
研究了树形实例上的买批量设施定位问题，提出针对单位需求和可分割变体的PTAS，并为电缆不可分割需求提供了近似算法。
0 citationsRead paper
Learning Faster without Deeper Networks: A*-Inspired Batch Selection for Efficient CNN Training
Traditional CNN training relies on random mini-batch sampling, which often leads to rapid saturation of learning signals as most samples quickly become “easy,” thereby slowing convergence. This work proposes A*-inspired Batch Selection (A*-BS), the first approach to integrate A* search into batch selection, introducing a dynamic scoring mechanism that jointly considers sample loss difficulty and reuse penalties to adaptively select informative and diverse batches. Without modifying network architecture or optimizer, A*-BS achieves superior performance on lightweight CNNs across half of the MedMNIST-v2 benchmark tasks, outperforming ResNet-18/50 in both accuracy and AUC—by up to 15% relatively—while significantly accelerating training. These results demonstrate that intelligent batch sequencing can partially substitute for model depth.
0 citationsRead paper