Probabilistic Models for Automatic Equivariant Feature Discovery

自动等变特征发现的概率模型

• 批准号: 436054-2013
• 负责人: Courville, Aaron
• 金额: $ 1.09万
• 依托单位: Université de Montréal
• 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=533417
• 关键词: Probabilistic; Models; Automatic; Equivariant; Feature

The performance of machine learning methods is often heavily dependent on how the input data is represented, making the choice of data representation a key aspect of many applications. The field of representation learning seeks to answer questions surrounding how we can best learn meaningful representations of data. Recent interest in applying deep models to representation learning has been fueled by successes in speech recognition, categorization of object in images and language tasks such as paraphrase detection.

机器学习方法的性能通常严重依赖于输入数据的表示方式，这使得数据表示的选择成为许多应用程序的关键方面。表征学习领域旨在回答围绕我们如何最好地学习有意义的数据表征的问题。最近对将深度模型应用于表示学习的兴趣受到语音识别，图像中对象分类和语言任务（如释义检测）的成功的推动。