Добірка наукової літератури з теми "Abstention mechanisms"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Abstention mechanisms".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Abstention mechanisms":
Katz, Gabriel, and Ines Levin. "A General Model of Abstention Under Compulsory Voting." Political Science Research and Methods 6, no. 3 (November 15, 2016): 489–508. http://dx.doi.org/10.1017/psrm.2016.49.
Svolik, Milan W. "Voting Against Autocracy." World Politics 75, no. 4 (October 2023): 647–91. http://dx.doi.org/10.1353/wp.2023.a908772.
Wilkens, Trine Levring, Zabrina Ziegler, Violetta Aru, Bekzod Khakimov, Snædís Lilja Overgaard, Søren Balling Engelsen, and Lars Ove Dragsted. "1–2 Drinks Per Day Affect Lipoprotein Composition after 3 Weeks—Results from a Cross-Over Pilot Intervention Trial in Healthy Adults Using Nuclear Magnetic Resonance-Measured Lipoproteins and Apolipoproteins." Nutrients 14, no. 23 (November 27, 2022): 5043. http://dx.doi.org/10.3390/nu14235043.
Minzer, Simona, Ricardo Arturo Losno, and Rosa Casas. "The Effect of Alcohol on Cardiovascular Risk Factors: Is There New Information?" Nutrients 12, no. 4 (March 27, 2020): 912. http://dx.doi.org/10.3390/nu12040912.
Braun, Daniela, and Markus Tausendpfund. "Electoral Behaviour in a European Union under Stress." Politics and Governance 8, no. 1 (February 13, 2020): 28–40. http://dx.doi.org/10.17645/pag.v8i1.2510.
Scussel, Fernanda, and Maribel Carvalho Suarez. "Consumer grief: understanding how consumers deal with the loss of extraordinary experiences." Cadernos EBAPE.BR 20, no. 3 (June 2022): 339–51. http://dx.doi.org/10.1590/1679-395120210046x.
Zeitzmann, Sebastian. "Towards an Ever More Differentiated Union? – Exit Strategies from Differentiated Integration." Zeitschrift für europarechtliche Studien 25, no. 4 (2022): 859–74. http://dx.doi.org/10.5771/1435-439x-2022-4-859.
Sholtz, Janae. "Bataille and Deleuze's Peculiar Askesis: Techniques of Transgression, Meditation and Dramatisation." Deleuze and Guattari Studies 14, no. 2 (May 2020): 198–228. http://dx.doi.org/10.3366/dlgs.2020.0399.
Krauss, Scott, David E. Stallknecht, Richard D. Slemons, Andrew S. Bowman, Rebecca L. Poulson, Jacqueline M. Nolting, James P. Knowles, and Robert G. Webster. "The enigma of the apparent disappearance of Eurasian highly pathogenic H5 clade 2.3.4.4 influenza A viruses in North American waterfowl." Proceedings of the National Academy of Sciences 113, no. 32 (July 25, 2016): 9033–38. http://dx.doi.org/10.1073/pnas.1608853113.
Kato, Gento. "When strategic uninformed abstention improves democratic accountability." Journal of Theoretical Politics 32, no. 3 (July 2020): 366–88. http://dx.doi.org/10.1177/0951629820926699.
Дисертації з теми "Abstention mechanisms":
Mayouf, Mouna Sabrine. "Intégration de connaissances de haut-niveau dans un système d'apprentissage par réseau de neurones pour la classification d'images." Electronic Thesis or Diss., Toulouse 3, 2023. http://www.theses.fr/2023TOU30341.
Neural networks have made remarkable improvements in challenging tasks such as automatic image classification and natural language processing. However, their black-box nature hinders explainability and limits their ability to leverage external knowledge. The purpose of this thesis is to explore and propose techniques for integrating knowledge into neural networks in order to improve their performance and interpretability. The first part of the thesis focuses on integrating knowledge at the input level. The first chapter deals with data preparation. A formalization of pre-processing is proposed to ensure the transparency and reproducibility of this step. This formalization enables us to study the impact of data augmentation: to characterize a good data preparation, and the informative state of a dataset, a set of measures and principles is proposed, then experimental protocols are designed to evaluate these principles on the BreakHis dataset. The second chapter of this part focuses on exploiting high-level knowledge to determine the order in which data should be inserted into the network. We introduce an incremental curriculum learning for ordering the input data. The results obtained show an improvement of accuracy and convergence speed. Although this study is carried out on the BreakHis dataset, we believe that it can be generalized to any other dataset. The second part is devoted to the integration of knowledge within the network architecture and at the output level. In this context, we focus on hierarchical multi-label classification, for which we formalize the knowledge representing the hierarchical link. For this aim, we introduce two constraints: one representing the fact that an object can only be assigned to one class at a given level of the hierarchy, and the other imposing that the global assignment of an object respects the class hierarchy (for example, we forbid classifying an element as a bee for its sub-type and a mammal for its super-type). We design an architecture and a loss function that impose these two constraints during learning. The architecture differs from the state of the art in that a single network is used to simultaneously predict the labels of the different levels: all layers are responsible for predicting the tuple of classes. Several variants of the network have been tested on five different datasets and the results confirm the efficiency of the hierarchical constraints, thus supporting the importance of taking external knowledge into account. In order to refine the results of this hierarchical classification, we introduce an abstention mechanism, in the form of a third constraint that enforces the network to give a prediction at the most precise level of specificity on which its confidence is sufficient and to abstain otherwise. We define different confidence thresholds and proposed different constraints on the thresholds accordingly to the class hierarchy. To evaluate this mechanism, new classification metrics that take abstention into account are defined. We carry out experiments on the same five datasets and the results show the interest of abstention, and the need to define empirical thresholds adapted to each dataset. In conclusion, the work in this thesis highlights the value of exploiting external knowledge, this is true for the three main components of a neural network: at the input level during data preparation, in the structure of the network, and at the output level when classification decisions are made
Частини книг з теми "Abstention mechanisms":
Kushwaha, Shekhar Prakash, Pradeep Kumar Sharma, and Sokindra Kumar. "Emerging Strategies in Antibacterial Drug Resistance Management Mechanisms." In Frontiers in Combating Antibacterial Resistance, 274–99. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-4139-1.ch011.