Deep Learning in Computer Vision: Methods, Interpretation, Causation, and Fairness-Reference-Cited by-同舟云学术

Deep Learning in Computer Vision: Methods, Interpretation, Causation, and Fairness

Published:2019-10 Issue: Volume: Page:73-100
ISSN:
Container-title:Operations Research & Management Science in the Age of Analytics
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Author:

Malik Nikhil¹,Singh Param Vir²

Affiliation:

1. Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213;

2. Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

Publisher

INFORMS

Reference60 articles.

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2. S. Arora, A. Bhaskara, R. Ge, and T. Ma. Provable bounds for learning some deep representations. E. P. Xing and T. Jebara, eds. Proceedings of the 31st International Conference on Machine Learning. PMLR, Beijing, China 584–592, 2014.

3. Neural networks and principal component analysis: Learning from examples without local minima

4. Gradient Descent with Identity Initialization Efficiently Learns Positive-Definite Linear Transformations by Deep Residual Networks

5. Y. Bengio and O. Delalleau. On the expressive power of deep architectures. J. Kivinen, C. Szepesvári, E. Ukkonen, and T. Zeugmann, eds. Algorithmic Learning Theory: International Conference on Algorithmic Learning Theory, Lecture Notes in Computer Science, Vol. 6925. Springer, Berlin, 18–36, 2011.

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