English(EN) Differentiable Knapsack and Top-k Operators via Dynamic Programming

新框架通过动态规划实现可微背包问题和 Top-k 算子

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员开发了一个新框架，将背包问题和 Top-k 算子视为动态规划问题，从而能够将它们集成到神经网络中。该方法使用可微松弛来克服这些离散算子中的零梯度挑战。该框架包括用于前向和后向传播的高效并行算法、关于正则化选择的理论见解，以及在学习背包解决方案和约束强化学习等任务上的实验验证。 AI

影响在神经网络中实现了更复杂的离散优化，有可能提高特征选择和强化学习等领域的性能。

排序理由该集群包含一篇详细介绍新算法框架和理论见解的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Germain Vivier-Ardisson, Micha\"el E. Sander, Axel Parmentier, Mathieu Blondel · 2026-11-06 05:00

动态规划实现可微分背包和 Top-k 算子

arXiv:2601.21775v2 Announce Type: replace Abstract: Knapsack and Top-k operators are useful for selecting discrete subsets of variables. However, their integration into neural networks is challenging as they are piecewise constant, yielding gradients that are zero almost everywhe…