Results 121 to 130 of about 204,288 (176)
A cross-entropy corrected hybrid multiconfiguration pair-density functional theory for complex molecular systems. [PDF]
Nat CommunFeng R, Zhang IY, Xu X.
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Cross Entropy in Deep Learning of Classifiers Is Unnecessary-ISBE Error Is All You Need. [PDF]
Entropy (Basel)Skarbek W.
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Commentary on: Aggadi N, Zeller K, Busey T. Quantifying the strength of firearms comparisons based on error rate studies. J Forensic Sci. 2024;70(1):84-97. https://doi.org/10.1111/1556-4029.15646; Warren EC, Handley JC, Sheets HD. Cross entropy and log likelihood ratio cost as performance measures for multi-conclusion categorical outcomes scales. J Forensic Sci. 2024;70(2):589-606. https://doi.org/10.1111/1556-4029.15686. [PDF]
J Forensic SciMorrison GS.
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Journal of Neuroscience Methods, 2017
Complexity measures for time series have been used in many applications to quantify the regularity of one dimensional time series, however many dynamical systems are spatially distributed multidimensional systems.We introduced Dynamic Cross-Entropy (DCE) a novel multidimensional complexity measure that quantifies the degree of regularity of EEG signals
Dorian, Aur, Fidel, Vila-Rodriguez
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Complexity measures for time series have been used in many applications to quantify the regularity of one dimensional time series, however many dynamical systems are spatially distributed multidimensional systems.We introduced Dynamic Cross-Entropy (DCE) a novel multidimensional complexity measure that quantifies the degree of regularity of EEG signals
Dorian, Aur, Fidel, Vila-Rodriguez
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Cross-entropy optimization for neuromodulation
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016This study presents a reinforcement learning approach for the optimization of the proportional-integral gains of the feedback controller represented in a computational model of epilepsy. The chaotic oscillator model provides a feedback control systems view of the dynamics of an epileptic brain with an internal feedback controller representative of the ...
Harleen K, Brar +3 more
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2022 17th Canadian Workshop on Information Theory (CWIT), 2022
The Rényi cross-entropy measure between two distributions, a generalization of the Shannon cross-entropy, was recently used as a loss function for the improved design of deep learning generative adversarial networks. In this work, we examine the properties of this measure and derive closed-form expressions for it when one of the distributions is fixed ...
Thierrin, Ferenc Cole +2 more
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The Rényi cross-entropy measure between two distributions, a generalization of the Shannon cross-entropy, was recently used as a loss function for the improved design of deep learning generative adversarial networks. In this work, we examine the properties of this measure and derive closed-form expressions for it when one of the distributions is fixed ...
Thierrin, Ferenc Cole +2 more
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Minimum cross entropy thresholding
Pattern Recognition, 1993Abstract The threshold selection problem is solved by minimizing the cross entropy between the image and its segmented version. The cross entropy is formulated in a pixel-to-pixel basis between the two images and a computationally attractive algorithm employing the histogram is developed.
C.H. Li, C.K. Lee
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2009
The cross-entropy (CE) method [56] is a general Monte Carlo approach to combinatorial and continuous multi-extremal optimization [195]. The method originated from the field of rare event simulation, where very small probabilities need to be accurately estimated.
Wesam Ashour Barbakh +2 more
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The cross-entropy (CE) method [56] is a general Monte Carlo approach to combinatorial and continuous multi-extremal optimization [195]. The method originated from the field of rare event simulation, where very small probabilities need to be accurately estimated.
Wesam Ashour Barbakh +2 more
openaire +1 more source