完全贝叶斯的自编码器与隐藏的稀疏高斯过程
Ba-Hien Tran1, Babak Shahbaba2, Stephan Mandt2
1Department of Data Science, EURECOM, France.
概括
我们开发了一个贝叶斯自编码器,使用摊销的MCMC来进行高效的推理. 该模型增强了动态表示学习和生成任务,具有灵活的priors和更好的可扩展性.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算统计学 计算统计学
背景情况:
- 自动编码器是表达式学习的强大工具,但往往缺乏概率的严谨性.
- 现有的贝叶斯自编码模型可能会遭受高推理成本的影响.
- 整合高斯过程与自动编码器已经显示出承诺,但面临着可扩展性挑战.
研究的目的:
- 引入一个完全贝叶斯自编码模型,具有高效的推理.
- 为了实现灵活的先前规范和后续近似.
- 为了提高可扩展性和处理复杂的数据结构,如缺失值.
主要方法:
- 一个完全贝叶斯的自编码器处理局部隐性变量和全球参数概率.
- 使用隐性随机网络进行后续采样的折旧马尔科夫链蒙特卡洛 (MCMC).
- 在潜伏空间上纳入稀疏高斯过程 (GP) 和深度高斯过程 (DGP) 的先验.
主要成果:
- 通过灵活的前置和后置近似来证明低推断成本.
- 通过贝叶斯对GP诱导点和超参数的贝叶斯处理,实现了更好的可扩展性.
- 在动态表示学习和生成建模任务中表现出强的表现.
结论:
- 拟议的贝叶斯自动编码器为表示和生成学习提供了一个强大的和可扩展的框架.
- 该模型有效地处理复杂的先验和缺失的数据,优于现有的GP-autoencoder混合动力.
- 这项工作通过集成先进的贝叶斯非参数来推进概率深度学习.
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