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新的主动学习方法解决了复杂的玻尔兹曼分布问题

研究人员开发了一种名为 AB-SID-iVAR 的新基于高斯过程的主动学习采集函数。该方法解决了在自诱导玻尔兹曼分布下学习未知函数的问题,这在计算化学中很常见,但由于目标分布的未知性和难以处理性而变得困难。所提出的方法在无需估计配分函数的情况下近似贝叶斯目标分布,使其适用于离散和连续域。实验结果表明,在合成基准测试以及 PES 建模和药物发现的实际任务中,该方法优于现有方法。 AI

影响 引入了一种处理复杂分布中主动学习的新方法,有望提高科学建模和药物发现的效率。

排序理由 该集群包含一篇详细介绍主动学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的主动学习方法解决了复杂的玻尔兹曼分布问题

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该集群包含一篇详细介绍主动学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Matthias Sachs ·

    自诱导玻尔兹曼权重下的高斯过程回归主动学习

    We consider the active learning problem where the goal is to learn an unknown function with low prediction error under an unknown Boltzmann distribution induced by the function itself. This self-induced weighting arises naturally in problems such as potential energy surface (PES)…