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Visualizing Visual Adaptation
Published on: April 24, 2017
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通过Rényi绑定优化和多源适应进行变化推理
Dana Zalman Oshri1,2, Shai Fine2
1School of Computer Science, Reichman University, Herzliya 4610101, Israel.
Entropy (Basel, Switzerland)
|October 28, 2023
概括
研究人员推出了一种新的变量雷尼日日志上限 (VRLU) 和变量雷尼日三明治 (VRS) 方法用于变量推理. 这种方法改善了现有的界限,提供了更严格的错误界限和在多源适应任务中提高了性能.
科学领域:
- 机器学习 机器学习
- 概率模型可能模型
- 优化优化 优化优化
背景情况:
- 变量推理通过优化近似概率密度,通常通过最大化证据下界 (ELBO).
- 基于蒙特卡洛近似的现有方法,如变量雷尼 (VR) 和基平方边界,受到低估或高方差的影响.
- 这些局限性阻碍了复杂模型中精确的密度近似.
研究的目的:
- 引入一个新的上限,变量雷尼日志上限 (VRLU),在蒙特卡洛近似下保留上限属性.
- 开发一个嵌入式变量推理方法 (Variational Rényi Sandwich - VRS) 来共同优化上下边界.
- 评估VRLU绑定和VRS方法与已建立的技术,如变量自编码器 (VAE) 和VR方法,特别是多源适应 (MSA).
主要方法:
- 提出了变量Renyi日志上限 (VRLU) 作为对现有的变量边界的改进.
- 开发了变量雷尼三明治 (VRS) 方法,用于同时优化上下界限.
- 进行了比较VRLU和VRS与VAE和VR方法的实验,包括对MSA的理论和经验分析.
主要成果:
- 与以前的边界不同的是,VRLU边界在蒙特卡罗近似下保留了它的上边界属性.
- 在多源适应任务中,VRS方法显示了更好的性能和更严格的错误界限.
- 经验和理论结果验证了与领先的MSA方法相比,VRS的有效性.
结论:
- VRLU和VRS方法在变异推理方面取得了重大进展,解决了现有边界的局限性.
- VRS为密度估计和域调整提供了一个强大的框架,特别是在具有挑战性的多源调整场景中.
- 提出的方法显示了改善现实世界的应用程序与异质数据源的预测建模的希望.
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