Stanford Brain Project


Selected papers:

  • Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations.
    Honglak Lee, Roger Grosse, Rajesh Ranganath and Andrew Y. Ng.
    ICML 2009. [PDF]

  • Large-scale Deep Unsupervised Learning using Graphics Processors.
    Rajat Raina, Anand Madhavan and Andrew Y. Ng.
    ICML 2009. [PDF]

  • Exponential Family Sparse Coding with Application to Self-taught Learning.
    Honglak Lee, Rajat Raina, Alex Teichman and Andrew Y. Ng.
    IJCAI 2009. [PDF]

  • Sparse Deep Belief Net Model for Visual Area V2.
    H. Lee, Chaitanya Ekanadham and A. Y. Ng.
    NIPS 2008. [PDF]

  • Exponential family sparse coding with application to self-taught learning with text documents.
    H. Lee and R. Raina and A. Teichman and A. Y. Ng.
    ICML Workshop on Prior Knowledge for Text and Language, 2008. [PDF]

  • Shift-Invariant Sparse Coding for Audio Classification.
    R. Grosse, R. Raina, H. Kwong and A. Y. Ng.
    Uncertainty in Artificial Intelligence (UAI), 2007. [PDF]

  • Self-taught learning: Transfer learning from unlabeled data.
    R. Raina, A. Battle, H. Lee, B. Packer and A. Y. Ng.
    ICML 2007. [PDF]

  • Efficient sparse coding algorithms.
    Honglak Lee, Alexis Battle, Raina Rajat and Andrew Y. Ng.
    NIPS 2007. [PDF]