Results 31 to 40 of about 10,752 (214)
Model Fusion with Kullback--Leibler Divergence
CoRR, 2020 We propose a method to fuse posterior distributions learned from heterogeneous datasets. Our algorithm relies on a mean field assumption for both the fused model and the individual dataset posteriors and proceeds using a simple assign-and-average approach.
Sebastian Claici +3 more
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Dynamic fine‐tuning layer selection using Kullback–Leibler divergence
Engineering Reports, 2023 The selection of layers in the transfer learning fine‐tuning process ensures a pre‐trained model's accuracy and adaptation in a new target domain. However, the selection process is still manual and without clearly defined criteria. If the wrong layers in
Raphael Ngigi Wanjiku +2 more
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Algorithms, 2022 The family of α-divergences including the oriented forward and reverse Kullback–Leibler divergences is often used in signal processing, pattern recognition, and machine learning, among others.
Frank Nielsen
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Optimism in reinforcement learning and Kullback-Leibler divergence [PDF]
2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2010 We consider model-based reinforcement learning in finite Markov De- cision Processes (MDPs), focussing on so-called optimistic strategies. In MDPs, optimism can be implemented by carrying out extended value it- erations under a constraint of consistency with the estimated model tran- sition probabilities. The UCRL2 algorithm by Auer, Jaksch and Ortner (
Sarah Filippi +2 more
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The AIC Criterion and Symmetrizing the Kullback–Leibler Divergence [PDF]
IEEE Transactions on Neural Networks, 2007 The Akaike information criterion (AIC) is a widely used tool for model selection. AIC is derived as an asymptotically unbiased estimator of a function used for ranking candidate models which is a variant of the Kullback-Leibler divergence between the true model and the approximating candidate model.
Abd-Krim Seghouane, Shun-ichi Amari
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On the Jensen–Shannon Symmetrization of Distances Relying on Abstract Means
Entropy, 2019 The Jensen–Shannon divergence is a renowned bounded symmetrization of the unbounded Kullback–Leibler divergence which measures the total Kullback–Leibler divergence to the average mixture distribution.
Frank Nielsen
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Algorithms for Nonnegative Matrix Factorization with the Kullback–Leibler Divergence [PDF]
Journal of Scientific Computing, 2021 Nonnegative matrix factorization (NMF) is a standard linear dimensionality reduction technique for nonnegative data sets. In order to measure the discrepancy between the input data and the low-rank approximation, the Kullback-Leibler (KL) divergence is one of the most widely used objective function for NMF.
Le, Thi Khanh Hien, Gillis, Nicolas
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Fault-tolerant relative navigation based on Kullback–Leibler divergence
International Journal of Advanced Robotic Systems, 2020 A fault-detection method for relative navigation based on Kullback–Leibler divergence (KLD) is proposed. Different from the traditional χ 2 -based approaches, the KLD for a filter is following a hybrid distribution that combines χ 2 distribution and F ...
Jun Xiong +6 more
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Zipf–Mandelbrot law, f-divergences and the Jensen-type interpolating inequalities
Journal of Inequalities and Applications, 2018 Motivated by the method of interpolating inequalities that makes use of the improved Jensen-type inequalities, in this paper we integrate this approach with the well known Zipf–Mandelbrot law applied to various types of f-divergences and distances, such ...
Neda Lovričević +2 more
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Android Malware Detection Using Kullback-Leibler Divergence
Advances in Distributed Computing and Artificial Intelligence Journal, 2015 Many recent reports suggest that mareware applications cause high billing to victims by sending and receiving hidden SMS messages. Given that, there is a need to develop necessary technique to identify malicious SMS operations as well as differentiate ...
Vanessa N. COOPER +2 more
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