Learning Hierarchical Generative Models: Theory and Applications

学习分层生成模型：理论与应用

• 批准号: 418196-2012
• 负责人: Salakhutdinov, Ruslan
• 金额: $ 1.46万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=531035
• 关键词: Learning; Hierarchical; Generative; Models; Theory

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.

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大幅增加。构建能够自动从此类数据中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大脑成像数据，或改进亚马逊等公司的产品推荐系统。