GPUs for unsupervised learning of representations
GPUs for unsupervised learning of representations
批准号:
389508-2010
负责人:
Bengio, Yoshua
金额:
$8.14万
依托单位:
依托单位国家:
加拿大
项目类别:
Research Tools and Instruments - Category 1 (<$150,000)
财政年份:
2009
资助国家:
加拿大
项目状态:
已结题
起止时间:
2009-01-01 至 2010-12-31
中文摘要
机器学习界最近提出了一种名为深度架构的新人工智能算法,这种算法有可能从网络和音乐收藏中的图像等海量数据中提取有意义的表示。图像的有意义的表示是捕捉人类关心的东西,例如,其中的对象,但对位置、照明、比例等因素的变化不太敏感。类似地,音乐的有意义的表示将音色等因素与所涉及的旋律内容和其他因素分开。
英文摘要
New artificial intelligence algorithms called deep architectures were recently proposed in the machine learning community that have the potential to extract meaningful representations from vast amounts of data such as the images in the web and music collections. A meaningful representation for an image is one that captures what humans care about, e.g., what are the objects in it, but is not too sensitive to changes due to factors such as position, illumination, scale, etc. Similarly, meaningful representations of music separate factors such as timbre from the melodic content and other factors involved.
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