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Готові списки джерел за темами / Constraint networks

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Добірка наукової літератури з теми "Constraint networks"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Constraint networks"

1

Brosowsky, Mathis, Florian Keck, Olaf Dünkel, and Marius Zöllner. "Sample-Specific Output Constraints for Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 6812–21. http://dx.doi.org/10.1609/aaai.v35i8.16841.

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It is common practice to constrain the output space of a neural network with the final layer to a problem-specific value range. However, for many tasks it is desired to restrict the output space for each input independently to a different subdomain with a non-trivial geometry, e.g. in safety-critical applications, to exclude hazardous outputs sample-wise. We propose ConstraintNet—a scalable neural network architecture which constrains the output space in each forward pass independently. Contrary to prior approaches, which perform a projection in the final layer, ConstraintNet applies an input-

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2

Rong, Zihao, Shaofan Wang, Dehui Kong, and Baocai Yin. "Improving object detection quality with structural constraints." PLOS ONE 17, no. 5 (2022): e0267863. http://dx.doi.org/10.1371/journal.pone.0267863.

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Recent researches revealed object detection networks using the simple “classification loss + localization loss” training objective are not effectively optimized in many cases, while providing additional constraints on network features could effectively improve object detection quality. Specifically, some works used constraints on training sample relations to successfully learn discriminative network features. Based on these observations, we propose Structural Constraint for improving object detection quality. Structural constraint supervises feature learning in classification and localization

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3

Zhang, Y., and R. H. C. Yap. "Set Intersection and Consistency in Constraint Networks." Journal of Artificial Intelligence Research 27 (December 13, 2006): 441–64. http://dx.doi.org/10.1613/jair.2058.

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In this paper, we show that there is a close relation between consistency in a constraint network and set intersection. A proof schema is provided as a generic way to obtain consistency properties from properties on set intersection. This approach not only simplifies the understanding of and unifies many existing consistency results, but also directs the study of consistency to that of set intersection properties in many situations, as demonstrated by the results on the convexity and tightness of constraints in this paper. Specifically, we identify a new class of tree convex constraints where

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4

Kharroubi, Idris, Thomas Lim, and Xavier Warin. "Discretization and machine learning approximation of BSDEs with a constraint on the Gains-process." Monte Carlo Methods and Applications 27, no. 1 (2021): 27–55. http://dx.doi.org/10.1515/mcma-2020-2080.

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Abstract We study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constr

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5

Dechter, Rina, Itay Meiri, and Judea Pearl. "Temporal constraint networks." Artificial Intelligence 49, no. 1-3 (1991): 61–95. http://dx.doi.org/10.1016/0004-3702(91)90006-6.

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6

Msaaf, Mohammed, and Fouad Belmajdoub. "Diagnosis of Discrete Event Systems under Temporal Constraints Using Neural Network." International Journal of Engineering Research in Africa 49 (June 2020): 198–205. http://dx.doi.org/10.4028/www.scientific.net/jera.49.198.

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The good functioning of a discrete event system is related to how much the temporal constraints are respected. This paper gives a new approach, based on a statistical model and neural network, that allows the verification of temporal constraints in DES. We will perform an online temporal constraint checking which can detect in real time any abnormal functioning related to the violation of a temporal constraint. In the first phase, the construction of temporal constraints from statistical model is shown and after that neural networks are involved in dealing with the online temporal constraint c

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7

Wang, Xiao Fei, Xi Zhang, Yue Bing Chen, Lei Zhang, and Chao Jing Tang. "Spectrum Assignment Algorithm Based on Clonal Selection in Cognitive Radio Networks." Advanced Materials Research 457-458 (January 2012): 931–39. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.931.

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An improved-immune-clonal-selection based spectrum assignment algorithm (IICSA) in cognitive radio networks is proposed, combing graph theory and immune optimization. It uses constraint satisfaction operation to make encoded antibody population satisfy constraints, and realizes the global optimization. The random-constraint satisfaction operator and fair-constraint satisfaction operator are designed to guarantee efficiency and fairness, respectively. Simulations are performed for performance comparison between the IICSA and the color-sensitive graph coloring algorithm. The results indicate tha

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8

Buscema, Massimo. "Constraint Satisfaction Neural Networks." Substance Use & Misuse 33, no. 2 (1998): 389–408. http://dx.doi.org/10.3109/10826089809115873.

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9

Suter, D. "Constraint networks in vision." IEEE Transactions on Computers 40, no. 12 (1991): 1359–67. http://dx.doi.org/10.1109/12.106221.

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10

Gottlob, Georg. "On minimal constraint networks." Artificial Intelligence 191-192 (November 2012): 42–60. http://dx.doi.org/10.1016/j.artint.2012.07.006.

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