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Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization
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“Proposition 4. Assume that f θ achieves the optimum value for I NWJ .Then the variance of the empirical NWJ estimator satisfies Var ÎNWJ ≥ e I(X;Y ) -1 N”
From A Theory of Usable Information Under Computational Constraints (2020) · cites this paper as Nguyen et al., 2010
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“Least squares generative adversarial networks,”
Mao, X., Li, Q., Xie, H., Lau, R. Y. K., Wang, Z., & Smolley, S. P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.304 · Cited by 5,437
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Nowozin, S., Cseke, B., & Tomioka, R. (2016) · arXiv · doi:10.48550/arxiv.1606.00709 · Cited by 1,183
A Theory of Usable Information Under Computational Constraints
Xu, Y., Zhao, S., Song, J., Stewart, R., & Ermon, S. (2020) · arXiv · doi:10.48550/arxiv.2002.10689 · Cited by 142
Autoregressive Unsupervised Image Segmentation
Ouali, Y., Hudelot, C., & Tami, M. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58571-6_9 · Cited by 75
Metadata
- Authors
- Nguyen, X., Wainwright, M. J., & Jordan, M. I.
- Venue
- IEEE Transactions on Information Theory
- Published
- 2010-11
- Volume
- 56
- Issue
- 11
- Pages
- 5847-5861
- ISSN
- 0018-9448
- 1557-9654
- Cited by
- 379
- References
- 1
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