随机旋转嵌入贝叶斯优化,用于人类在循环中的个性化音乐生成
Miguel Marcos1, Lorenzo Mur-Labadia1, Ruben Martinez-Cantin1
1Departamento de Informática e Ingeniería de Sistemas, Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza, Zaragoza, Spain.
PloS one
|November 21, 2025
概括
我们开发了随机旋转嵌入贝叶斯优化 (ROMBO) 来个性化生成深度学习模型. 罗姆博高效优化了高维空间,提高了用户在音乐生成任务中的满意度.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算创造力的创造力
背景情况:
- 生成型深度学习模型通过隐性空间采样创建多样化的输出.
- 个性化这些模型需要有效优化隐藏空间内的用户偏好.
- 贝叶斯优化是人类在循环优化的关键技术.
研究的目的:
- 引入随机旋转嵌入贝叶斯优化 (ROMBO) 进行高效的高维优化.
- 通过在生成模型中优化用户查询来实现个性化的内容生成.
- 评估ROMBO在音乐生成任务中的有效性.
主要方法:
- 开发了ROMBO,使用随机旋转将低维的高斯空间嵌入高维空间.
- 应用ROMBO来优化对生成深度学习音乐模型的查询.
- 进行模拟实验和用户研究 (n=16) 进行评估.
主要成果:
- 罗姆博在高维优化方面表现出比基线方法更好的性能.
- 在模拟音乐生成任务中实现了16% - 31%的损失减少.
- 用户研究表明,在找到最喜欢的音乐方面增加了40%,发现速度更快了16%,不喜欢的音乐的时间减少了18%.
结论:
- 罗姆博提供了一种高效有效的方法来个性化生成型深度学习模型.
- 这种方法显著提高了用户体验和对内容生成任务的满意度.
- 对于在高维,旋转对称的空间中需要样本效率优化的应用,ROMBO显示出有前途.
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