用深度学习加速参数空间扫描，提升收敛速度与泛化能力
DLScanner: A parameter space scanner package assisted by deep learning methods
- 用相似性学习网络优化采样点表示，使目标内点聚类、外点分离
- 动态采样策略结合VEGAS映射，自适应推荐新采样点并提升映射精度
- 相比传统方法，扫描效率与收敛性能显著提升，适合高维参数空间探索
本文提出一种由深度学习技术增强的参数空间扫描工具包。针对已有深度学习方法存在的两大问题——高维扫描收敛慢、网络泛化能力有限——我们设计了相应解决方案。首先，引入相似性学习网络，将采样点映射到表示空间，在该空间中目标内点聚集，而目标外点被有效分离，从而改善采样点表征并加速收敛。其次，通过集成动态采样策略，结合VEGAS映射，自适应生成新采样点，并在收集更多点后持续优化映射效果。实验表明，所提框架在性能和效率上均显著优于其他扫描方法。
原文摘要 · Abstract (English)
In this paper, we introduce a scanner package enhanced by deep learning (DL) techniques. The proposed package addresses two significant challenges associated with previously developed DL-based methods: slow convergence in high-dimensional scans and the limited generalization of the DL network when mapping random points to the target space. To tackle the first issue, we utilize a similarity learning network that maps sampled points into a representation space. In this space, in-target points are grouped together while out-target points are effectively pushed apart. This approach enhances the scan convergence by refining the representation of sampled points. The second challenge is mitigated by integrating a dynamic sampling strategy. Specifically, we employ a VEGAS mapping to adaptively suggest new points for the DL network while also improving the mapping when more points are collected. Our proposed framework demonstrates substantial gains in both performance and efficiency compared to other scanning methods.
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