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有读书笔记Unsupervised feature learning by deep sparse coding

mengyang1983 添加于 2014-11-26 22:03 | 1789 次阅读 | 0 个评论
  •  作 者

    Bengio Y, Courville A, Vincent P
  •  摘 要

    The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning.
  •  详细资料

    • 关键词: cs.LG
    • 文献种类: Manual Script
    • 期卷页: 2014
    • 日期: 2014-4-23
    • 发布方式: arXiv e-prints
    • 备注:arXiv:1206.5538v3
  • 学科领域 信息系统 » 计算机科学

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