Results 31 to 40 of about 7,533,665 (302)
Variational f-divergence Minimization
CoRR, 2019 Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific f-divergence between the model and data distribution. In light of recent successes in training Generative Adversarial Networks, alternative non-likelihood training criteria have been proposed.
Mingtian Zhang +4 more
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On change of measure inequalities for $f$-divergences [PDF]
, 2022 17 pagesWe propose new change of measure inequalities based on $f$-divergences (of which the Kullback-Leibler divergence is a particular case). Our strategy relies on combining the Legendre transform of $f$-divergences and the Young-Fenchel inequality ...
Picard-Weibel, Antoine, Guedj, Benjamin
core
Fragmentation functions for gluon into B c or B c ∗ $$ {B}_c^{\ast } $$ meson
Journal of High Energy Physics, 2022 In the paper, we calculate the fragmentation functions for g → B c and g → B c ∗ $$ {B}_c^{\ast } $$ . The ultraviolet divergences in the calculation are removed through the renormalization of the operator definition of the fragmentation functions under ...
Xu-Chang Zheng +2 more
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Refinement of the Jensen integral inequality
Open Mathematics, 2016 In this paper we give a refinement of Jensen’s integral inequality and its generalization for linear functionals. We also present some applications in Information Theory.
Sever Dragomir Silvestru +2 more
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On the f -divergence for non-additive measures
Fuzzy Sets and Systems, 2016 The f-divergence evaluates the dissimilarity between two probability distributions defined in terms of the Radon-Nikodym derivative of these two probabilities. The f-divergence generalizes the Hellinger distance and the Kullback-Leibler divergence among other divergence functions. In this paper we define an analogous function for non-additive measures.
Vicenç Torra +2 more
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Recoverability for optimized quantum f-divergences
, 2021 The optimized quantum f -divergences form a family of distinguishability measures that includes the quantum relative entropy and the sandwiched R\u27enyi relative quasi-entropy as special cases.
Wilde, Mark M., Gao, Li
core +1 more source
Remote Sensing, 2023 The accurate detection of collapsed buildings is of great significance for post-disaster rescue and reconstruction. High-resolution optical images are important data sources for identifying collapsed buildings, and the identification accuracy mainly ...
Ruoyang Liu, Wenquan Zhu, Xinyi Yang
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Ensemble estimation of multivariate f-divergence [PDF]
2014 IEEE International Symposium on Information Theory, 2014 f-divergence estimation is an important problem in the fields of information theory, machine learning, and statistics. While several divergence estimators exist, relatively few of their convergence rates are known. We derive the MSE convergence rate for a density plug-in estimator of f-divergence.
Kevin R. Moon, Alfred O. Hero III
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Law invariant risk measures and information divergences
Dependence Modeling, 2018 Aone-to-one correspondence is drawnbetween lawinvariant risk measures and divergences,which we define as functionals of pairs of probability measures on arbitrary standard Borel spaces satisfying a few natural properties.
Lacker Daniel
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On Data-Processing and Majorization Inequalities for f-Divergences with Applications
Entropy, 2019 This paper is focused on the derivation of data-processing and majorization inequalities for f-divergences, and their applications in information theory and statistics.
Igal Sason
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