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Carnegie Mellon Robotics Institute

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Abstract

Prior work has shown that features which appear to be biologically plausible as well as empirically useful can be found by sparse coding with a prior such as a laplacian that promotes sparsity. We show how smoother priors can preserve the benefits of these sparse priors while adding stability to the Maximum A-Posteriori (MAP) estimate that makes it more useful for prediction problems. Additionally, we show how to calculate the derivative of the MAP estimate efficiently with implicit differentiation. One prior that can be differentiated this way is KL-regularization. We demonstrate its effectiveness on a wide variety of applications, and find that online optimization of the parameters of the KL-regularized model can significantly improve prediction performance.

Keywords

sparse coding, sparse approximation, semi-supervised learning, machine learning

Notes

Sponsor: NDSEG Fellowship

Text Reference

David Bradley and J. Andrew (Drew) Bagnell, "Differentiable Sparse Coding," Proceedings of Neural Information Processing Systems 22, December, 2008.

BibTeX Reference

@inproceedings{Bradley_2008_6192,
   author = "David Bradley and J. Andrew (Drew) Bagnell",
   title = "Differentiable Sparse Coding",
   booktitle = "Proceedings of Neural Information Processing Systems 22",
   month = "December",
   year = "2008",
}

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