Learning Hierarchical Generative Models: Theory and Applications

近年来，计算能力和可从网络、摄像机、高通量基因组测序技术和各种实验室测量获得的数据量有了巨大的增长。构建能够从这些数据中自动发现有意义的表示的智能系统，应该会带来新的科学发现。例如，它们可以帮助神经科学家分析高维fMRI脑成像数据，或者改进亚马逊等公司的产品推荐系统。

In recent years, there has been a massive increase in computational power and the amount of data available from the web, video cameras, high-throughput genomic sequencing technologies, and various laboratory measurements. Building intelligent systems that can automatically discovery meaningful representations from such data, should lead to new scientific discoveries. For example, they can help neuroscientists analyze high-dimensional fMRI brain imaging data, or improve product recommendation systems of companies like Amazon.