report · 2014

Discovery of Deep Structure from Unlabeled Data

Cited by 0Open access

Abstract

This research project addressed the problem of learning useful "deep" representations from unlabeled data. The major goal was to innovate new unsupervised deep learning algorithms capable of learning important semantic structure in the input data in a domain general way. At the conclusion of this project, these goals stand fulfilled. The lab produced a variety of new and influential learning algorithms including Independent Subspace Analysis (ISA); Reconstruction Independent Components Analysis (RICA); recursive neural networks; and recursive tensor networks, among others. These algorithms have posted state-of-the-art results across a number of domains and tasks, and have had impact on both academia and industry.

Cite this paper

APA 7

Ng, A. Y., & Manning, C. D. (2014). Discovery of Deep Structure from Unlabeled Data. https://doi.org/10.21236/ada614158

MLA 9

Ng, Andrew Y., and Christopher D. Manning. “Discovery of Deep Structure from Unlabeled Data.” 2014, https://doi.org/10.21236/ada614158.

Chicago

Ng, Andrew Y., and Christopher D. Manning. 2014. “Discovery of Deep Structure from Unlabeled Data.” https://doi.org/10.21236/ada614158.

Harvard

Ng, A.Y. and Manning, C.D. (2014) ‘Discovery of Deep Structure from Unlabeled Data’. Available at: https://doi.org/10.21236/ada614158.

BibTeX

@article{ng2014discovery,
  title = {{Discovery of Deep Structure from Unlabeled Data}},
  author = {Ng, Andrew Y. and Manning, Christopher D.},
  year = {2014},
  doi = {10.21236/ada614158}
}

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