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IPF minimizes the strictly convex Kullback-Leibler divergence over the set of matrices with prescribed margins.
“IPF minimizes the strictly convex Kullback-Leibler divergence [15, 16] (see Sect. 5.2.2) over the set of matrices with the prescribed margins.”
From Classical Paradigm of Transportation Modeling (2026) · cites this paper as [16]
Section 2 (pp. 1–2) defines information and divergence between probability distributions, and Lemma 3.1 (p. 4) uses the positive second derivative of t log t to establish a strict equality condition. The paper does not address IPF or show that it minimizes divergence over matrices with prescribed margins. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIntroducing KL divergence loss between predictions of the quantized network and the full-precision network as soft labels recovers the performance of the quantized network.
“we introduce the Kullback-Leibler (KL) divergence loss [28] between the predictions of the quantized network and the full-precision network as soft labels to align their outputs, thereby recovering the performance of the quantized network”
From Task-Specific Zero-Shot Quantization-Aware Training for Object Detection (2025) · cites this paper as [28]
RWalk improves the regularization methods of EWC and SI and further improves performance, but it still retains some old data for performance reasons, which shows the effectiveness of rehearsal strategies for mitigating catastrophic forgetting.
“Rwalk improves the regularization methods of EWC and SI, and further improves the performance based on them. However, for performance reasons, some old data are still retained. This shows the effectiveness of rehearsal strategies for mitigating catastrophic forgetting.”
From An Appraisal of Incremental Learning Methods (2020) · cites this paper as [48]
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- [15]15. Journal, Vol. 27 (1948), pp. 379–423; pp. 623–656. - [17] B. L. WELCH, 'Note on discriminant functions," Biometrika, Vol.31 (1939), pp. 218–219. - [16] C. E. SHANNoN AND W. WEAvER, The Mathematical Theory of Communication, University of Illinois Press, Urbana, 1949. - [18] N. WiENER, Cybernetics, John Wiley and Sons, 1948. - [19] J. L. DooB, "Statistical estimation," Trans. Am. Math. Soc., Vol. 39 (1936), pp. 410–421. - [20] E. L. LEHmANN, "Some principles of the theory of testing hypotheses," Annals of Math. Stat., Vol. 21 (1950), pp. 1–26.
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Giantomassi, A., Ferracuti, F., Iarlori, S., Ippoliti, G., & Longhi, S. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2370936 · Cited by 168 · see citing works
Roux, F., Wibral, M., Singer, W., Aru, J., & Uhlhaas, P. J. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.5778-12.2013 · Cited by 169 · see citing works
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Richards, B. A., Xia, F., Santoro, A., Husse, J., Woodin, M. A., Josselyn, S. A., & Frankland, P. W. (2014) · Nature Neuroscience · doi:10.1038/nn.3736 · Cited by 150 · see citing works
Sun, S., & Xu, X. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2010.2093575 · Cited by 137 · see citing works
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Parr, T., Da Costa, L., & Friston, K. (2020) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2019.0159 · Cited by 141 · see citing works
Gruenerbl, A., Osmani, V., Bahle, G., Carrasco, J. C., Oehler, S., Mayora, O., Haring, C., & Lukowicz, P. (2014) · Proceedings of the 5th Augmented Human International Conference · doi:10.1145/2582051.2582089 · Cited by 139 · see citing works
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Kharazmi, P., Kalia, S., Lui, H., Wang, Z. J., & Lee, T. K. (2018) · Skin Research and Technology · doi:10.1111/srt.12422 · Cited by 83 · see citing works
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Nguyen, T. T., Huynh, T. T., Ren, Z., Nguyen, P. L., Liew, A. W.-C., Yin, H., & Nguyen, Q. V. H. (2022) · arXiv · doi:10.48550/arxiv.2209.02299 · Cited by 193 · see citing works
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Corcoran, A. W., Hohwy, J., & Friston, K. J. (2023) · Neuron · doi:10.1016/j.neuron.2023.08.027 · Cited by 26 · see citing works
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Tiede, P., Broderick, A. E., & Palumbo, D. C. M. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac3a6b · Cited by 29 · see citing works
Bauer, J., Kirschbaum, M. U. F., Weihermüller, L., Huisman, J. A., Herbst, M., & Vereecken, H. (2008) · Soil Biology and Biochemistry · doi:10.1016/j.soilbio.2008.07.024 · Cited by 29 · see citing works
- Authors
- Kullback, S., & Leibler, R. A.
- Venue
- The Annals of Mathematical Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 1951-03
- Volume
- 22
- Issue
- 1
- Pages
- 79-86
- ISSN
- 0003-4851 (ISSN-L)
- OpenAlex
- W1965555277
- Cited by
- 20,193
- References
- 0
- Citations by year
- 2026: 1111 · 2025: 1372 · 2024: 1320 · 2023: 1325 · 2022: 1331 · 2021: 1488 · 2020: 1323 · 2019: 1162 · 2018: 941 · 2017: 856 · 2016: 799 · 2015: 784 · 2014: 799 · 2013: 733 · 2012: 661
- FWCI
- 8.15
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Annals of Mathematical Statistics (publishedVersion)
- Project Euclid (Cornell University) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- information, sufficiency, sufficient statistics, information theory, statistical inference
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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