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English(EN) Advancing Optimal Subset Oracle via Learning Relaxation of Neural Set Functions

新方法推进神经集函数学习,降低计算开销

研究人员开发了一种新方法来改进神经集函数的学习,这对于药物发现和产品推荐等应用至关重要。该方法将证据下界重新解释为连续松弛,创建了一个替代目标,用以替代计算密集型的蒙特卡洛采样来进行梯度估计。这种学习到的替代目标提供了稳定高效的梯度,降低了开销并加速了推理,实验证明其优于现有方法。 AI

影响 该方法通过降低学习复杂集函数的计算开销,有望加速 AI 驱动的药物发现和产品推荐。

排序理由 该集群包含一篇发表在 arXiv 上的研究论文,详细介绍了一种学习神经集函数的新方法。

在 Hugging Face Daily Papers 阅读 →

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新方法推进神经集函数学习,降低计算开销

报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Yongquan Shi, Zijing Ou, Shiping Wang, Yatao Bian ·

    通过学习神经集函数松弛来推进最优子集预言机

    arXiv:2607.11555v1 Announce Type: new Abstract: Learning neural set functions is pivotal to a wide range of important applications, including compound selection in AI-driven drug discovery and product recommendation. Recent work has introduced optimal subset oracles to implicitly…

  2. arXiv cs.LG TIER_1 English(EN) · Yatao Bian ·

    通过学习神经集函数松弛来推进最优子集预言机

    Learning neural set functions is pivotal to a wide range of important applications, including compound selection in AI-driven drug discovery and product recommendation. Recent work has introduced optimal subset oracles to implicitly learn set functions under practical weakly supe…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    通过学习神经集函数松弛来推进最优子集预言机

    Learning neural set functions is pivotal to a wide range of important applications, including compound selection in AI-driven drug discovery and product recommendation. Recent work has introduced optimal subset oracles to implicitly learn set functions under practical weakly supe…