Results 21 to 30 of about 1,079,572 (317)
Applied Sciences, 2021 A study on object detection utilizing deep learning is in continuous progress to promptly and accurately determine the surrounding situation in the driving environment.
Seong-Eun Ryu, Kyung-Yong Chung
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Market Models with Optimal Arbitrage [PDF]
SIAM Journal on Financial Mathematics, 2015 We construct and study market models admitting optimal arbitrage. We say that a model admits optimal arbitrage if it is possible, in a zero-interest rate setting, starting with an initial wealth of 1 and using only positive portfolios, to superreplicate a constant c>1.
Chau, H.N., Tankov, P.
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Optimal designs for copula models [PDF]
Statistics, 2016 Copula modelling has in the past decade become a standard tool in many areas of applied statistics. However, a largely neglected aspect concerns the design of related experiments. Particularly the issue of whether the estimation of copula parameters can be enhanced by optimizing experimental conditions and how robust all the parameter estimates for the
Perrone, Elisa, Müller, Werner G.
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FPGAN: An FPGA Accelerator for Graph Attention Networks With Software and Hardware Co-Optimization
IEEE Access, 2020 The Graph Attention Networks (GATs) exhibit outstanding performance in multiple authoritative node classification benchmark tests (including transductive and inductive).
Weian Yan, Weiqin Tong, Xiaoli Zhi
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Optimizing Preventive Maintenance Models [PDF]
Computational Optimization and Applications, 2006 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Michael Bartholomew-Biggs +2 more
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Entropy-Optimized Texture Models [PDF]
, 2008 In order to robustly match a statistical model of shape and appearance (e.g. AAM) to an unseen image, it is crucial to employ a robust model fittness measure. Dense sampling of texture over the region covered by the shape of interest makes comparison of model and image in principle robust. However, when merely texture differences are taken into account,
Sebastian Zambal +2 more
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Model Optimization in Imbalanced Regression
, 2022 Imbalanced domain learning aims to produce accurate models in predicting instances that, though underrepresented, are of utmost importance for the domain. Research in this field has been mainly focused on classification tasks. Comparatively, the number of studies carried out in the context of regression tasks is negligible.
Anibal Silva +2 more
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Feedback-Based Validation Learning
ComputationThis paper presents Feedback-Based Validation Learning (FBVL), a novel approach that transforms the role of validation datasets in deep learning. Unlike conventional methods that utilize validation datasets for performance evaluation post-training, FBVL ...
Chafik Boulealam +4 more
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Improved Mask R-CNN Combined with Otsu Preprocessing for Rice Panicle Detection and Segmentation
Applied Sciences, 2022 Rice yield is closely related to the number and proportional area of rice panicles. Currently, rice panicle information is acquired with manual observation, which is inefficient and subjective.
Shilan Hong +5 more
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Optimization Models of Natural Communication [PDF]
Journal of Quantitative Linguistics, 2017 A family of information theoretic models of communication was introduced more than a decade ago to explain the origins of Zipf's law for word frequencies. The family is a based on a combination of two information theoretic principles: maximization of mutual information between forms and meanings and minimization of form entropy.
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