隐藏空间贝叶斯优化与隐藏数据增强用于增强探索
Onur Boyar1, Ichiro Takeuchi2,3
1Department of Mechanical Systems Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 4648603, Japan boyar.onur.nagoyaml@gmail.com.
Neural computation
|September 23, 2024
概括
通过解决变化自编码器 (VAE) 和贝叶斯优化 (BO) 之间的不匹配,提高了潜在空间贝叶斯优化 (LSBO). 我们的新方法在新的设计任务中提高了探索和采样效率.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算化学计算化学
背景情况:
- 潜空间贝叶斯优化 (LSBO) 将变化自编码器 (VAE) 等生成模型与贝叶斯优化 (BO) 集成,用于 de novo 对象生成.
- 由于VAE和BO目标之间的不匹配,现有的LSBO方法在勘探中扎,导致效率低下.
- 这种不匹配造成了潜在的不一致性,阻碍了LSBO的有效性.
研究的目的:
- 介绍并解决LSBO中隐藏不一致的问题.
- 提高LSBO的勘探能力和采样效率.
- 开发一种新的LSBO框架,以改善新一代的LSBO.
主要方法:
- 提出了潜在一致性/不一致性的概念来诊断LSBO挑战.
- 开发了一个潜在一致的意识获取函数 (LCA-AF),以利用一致的潜在点.
- 引入LCA-VAE,这是一个VAE变体,采用隐性空间数据增强和不一致性惩罚来促进隐性一致性.
- 结合了LCA-VAE和LCA-AF,创建了LCA-LSBO框架.
主要成果:
- 拟议的LCA-LSBO框架显示了高样本效率.
- 通过解决潜在的不一致性,可以实现有效的勘探能力.
- 在LCA-VAE中新的隐性空间数据增强的整合显著改善了LSBO性能.
结论:
- 解决潜在一致性对于提高LSBO效率和勘探至关重要.
- 结合了LCA-VAE和LCA-AF的LCA-LSBO,为新一代任务提供了一个强大的解决方案.
- 这项研究强调了潜在空间数据增强在增强基于生成模型的优化方面的有效性.
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