Results 21 to 30 of about 389,136 (225)
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation [PDF]
International Joint Conference on Artificial Intelligence, 2021 Knowledge distillation (KD), transferring knowledge from a cumbersome teacher model to a lightweight student model, has been investigated to design efficient neural architectures.
Taehyeon Kim +4 more
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Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer's dementia. [PDF]
Eur J Nucl Med Mol Imaging, 2020 Purpose Positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) reveals altered cerebral metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer’s dementia (AD). Previous metabolic connectome analyses derive from groups
Wang M +10 more
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Entropy, 2022 By calculating the Kullback–Leibler divergence between two probability measures belonging to different exponential families dominated by the same measure, we obtain a formula that generalizes the ordinary Fenchel–Young divergence.
Frank Nielsen
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The Kullback-Leibler Divergence Class in Decoding the Chest Sound Pattern [PDF]
Informatică economică, 2019 Kullback-Leibler Divergence Class or relative entropy is a special case of broader divergence. It represents a calculation of how one probability distribution diverges from another one, expected probability distribution. Kullback-Leibler divergence has a
Antonio CLIM, Razvan Daniel ZOTA
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Tracking changes using Kullback-Leibler divergence for the continual learning [PDF]
IEEE International Conference on Systems, Man and Cybernetics, 2022 Recently, continual learning has received a lot of attention. One of the significant problems is the occurrence of concept drift, which consists of changing probabilistic characteristics of the incoming data.
Sebastian Basterrech, Michal Wo'zniak
semanticscholar +1 more source
A Kullback–Leibler divergence method for input–system–state identification [PDF]
Journal of Sound and Vibration, 2023 The capability of a novel Kullback-Leibler divergence method is examined herein within the Kalman filter framework to select the input-parameter-state estimation execution with the most plausible results.
Marios Impraimakis
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Statistical Estimation of the Kullback–Leibler Divergence
Mathematics, 2021 Asymptotic unbiasedness and L2-consistency are established, under mild conditions, for the estimates of the Kullback–Leibler divergence between two probability measures in Rd, absolutely continuous with respect to (w.r.t.) the Lebesgue measure.
Alexander Bulinski, Denis Dimitrov
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Annual Meeting of the Association for Computational Linguistics, 2023 In this paper, we address the task of cloze-style multiple choice question (MCQs) distractor generation. Our study is featured by the following designs. First, we propose to formulate the cloze distractor generation as a Text2Text task.
Haibo Wang +6 more
semanticscholar +1 more source
Parameter Estimation Based on Cumulative Kullback–Leibler Divergence
Revstat Statistical Journal, 2021 In this paper, we propose some estimators for the parameters of a statistical model based on Kullback–Leibler divergence of the survival function in continuous setting and apply it to type I censored data.
Yaser Mehrali , Majid Asadi
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Model Averaging Estimation Method by Kullback–Leibler Divergence for Multiplicative Error Model
Complexity, 2022 In this paper, we propose the model averaging estimation method for multiplicative error model and construct the corresponding weight choosing criterion based on the Kullback–Leibler divergence with a hyperparameter to avoid the problem of overfitting ...
Wanbo Lu, Wenhui Shi
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