用主动学习约束高维模型参数
Constraining the Parameters of High-Dimensional Models with Active Learning
摘要：
用$>5-10$参数约束物理模型的参数是粒子物理和天文学等领域普遍存在的问题。生成数据以探索该参数空间通常需要大量的计算资源。通常采用的减少相关物理参数数目的解决方案妨碍了结果的通用性。在本文中，我们表明，这个问题可以缓解使用主动学习。我们用高能物理的例子来说明这一点，高能物理是一个模拟通常很昂贵的领域，参数空间是高维的。我们表明，主动学习技术查询由委员会和查询由辍学委员会允许识别模型点在高维参数空间的有趣的区域（例如，周围的决策边界）。这使得有可能比目前使用最常见的采样算法更有效地约束模型参数，并在相同数量的数据上训练性能更好的机器学习模型。实现本文实验的代码可以在GitHub上找到。
英文摘要：
Constraining the parameters of physical models with $>5-10$ parameters is a widespread problem in fields like particle physics and astronomy. The generation of data to explore this parameter space often requires large amounts of computational resources. The commonly used solution of reducing the number of relevant physical parameters hampers the generality of the results. In this paper we show that this problem can be alleviated by the use of active learning. We illustrate this with examples from high energy physics, a field where simulations are often expensive and parameter spaces are high-dimensional. We show that the active learning techniques query-by-committee and query-by-dropout-committee allow for the identification of model points in interesting regions of high-dimensional parameter spaces (e.g. around decision boundaries). This makes it possible to constrain model parameters more efficiently than is currently done with the most common sampling algorithms and to train better performing machine learning models on the same amount of data. Code implementing the experiments in this paper can be found on GitHub.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
