Results 21 to 30 of about 10,677 (169)
Some bounds for skewed α-Jensen-Shannon divergence
Results in Applied Mathematics, 2019 Based on the skewed Kullback-Leibler divergence introduced in the natural language processing, we derive the upper and lower bounds on the skewed version of the Jensen-Shannon divergence and investigate properties of them.
Takuya Yamano
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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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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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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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Kullback–Leibler Divergence Measure for Multivariate Skew-Normal Distributions
Entropy, 2012 The aim of this work is to provide the tools to compute the well-known Kullback–Leibler divergence measure for the flexible family of multivariate skew-normal distributions.
Reinaldo B. Arellano-Valle +1 more
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Organic Materials of Tomorrow: Horizons of Artificial Intelligence
Advanced Materials, EarlyView.This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
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Distributed Vector Quantization Based on Kullback-Leibler Divergence
Entropy, 2015 The goal of vector quantization is to use a few reproduction vectors to represent original vectors/data while maintaining the necessary fidelity of the data.
Pengcheng Shen +2 more
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