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Libri sul tema "Adaptation de domaines"

Autore: Grafiati

Pubblicato: 19 ottobre 2024

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1

Li, Jingjing, Lei Zhu e Zhekai Du. Unsupervised Domain Adaptation. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1025-6.

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2

Kamnitsas, Konstantinos, Lisa Koch, Mobarakol Islam, Ziyue Xu, Jorge Cardoso, Qi Dou, Nicola Rieke e Sotirios Tsaftaris, a cura di. Domain Adaptation and Representation Transfer. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16852-9.

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3

Singh, Richa, Mayank Vatsa, Vishal M. Patel e Nalini Ratha, a cura di. Domain Adaptation for Visual Understanding. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-30671-7.

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4

Koch, Lisa, M. Jorge Cardoso, Enzo Ferrante, Konstantinos Kamnitsas, Mobarakol Islam, Meirui Jiang, Nicola Rieke, Sotirios A. Tsaftaris e Dong Yang, a cura di. Domain Adaptation and Representation Transfer. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-45857-6.

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5

Csurka, Gabriela, a cura di. Domain Adaptation in Computer Vision Applications. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58347-1.

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6

Csurka, Gabriela, Timothy M. Hospedales, Mathieu Salzmann e Tatiana Tommasi. Visual Domain Adaptation in the Deep Learning Era. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-79175-8.

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7

Venkateswara, Hemanth, e Sethuraman Panchanathan, a cura di. Domain Adaptation in Computer Vision with Deep Learning. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45529-3.

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8

Søgaard, Anders. Semi-Supervised Learning and Domain Adaptation in Natural Language Processing. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-031-02149-7.

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9

Albarqouni, Shadi, Spyridon Bakas, Konstantinos Kamnitsas, M. Jorge Cardoso, Bennett Landman, Wenqi Li, Fausto Milletari et al., a cura di. Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60548-3.

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10

Bunt, Harry C. Trends in Parsing Technology: Dependency Parsing, Domain Adaptation, and Deep Parsing. Dordrecht: Springer Science+Business Media B.V., 2011.

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11

Albarqouni, Shadi, M. Jorge Cardoso, Qi Dou, Konstantinos Kamnitsas, Bishesh Khanal, Islem Rekik, Nicola Rieke et al., a cura di. Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87722-4.

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12

Wang, Qian, Fausto Milletari, Hien V. Nguyen, Shadi Albarqouni, M. Jorge Cardoso, Nicola Rieke, Ziyue Xu et al., a cura di. Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33391-1.

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13

Fenwick, John. The public domain in an English region: Aspects of adaptation and change in public authorities. Glasgow, Scotland: Centre for the Study of Public Policy, University of Strathclyde, 1989.

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14

Canada, Canada Environment. A decade of achievement : environment and energy research & development =: Dix années de succès : recherche et développement dans le domaine de l'environnement et de l'énergie. Hull, Qué: Environment Canada = Environnement Canada, 1989.

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15

Wallberg, Günther. Die Schutzfähigkeit von Bearbeitungen gemeinfreier Werke der Ernsten Musik im europäischen Vergleich. Frankfurt am Main: P. Lang, 2002.

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16

Redko, Ievgen, Amaury Habrard, Marc Sebban, Emilie Morvant e Younès Bennani. Advances in Domain Adaptation Theory. Elsevier, 2019.

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17

Ratha, Nalini, Vishal M. Patel, Richa Singh e Mayank Vatsa. Domain Adaptation for Visual Understanding. Springer, 2020.

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18

Gopalan, Raghuraman, Ruonan Li, Rama Chellappa e Vishal M. Patel. Domain Adaptation for Visual Recognition. Now Publishers, 2015.

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19

Ratha, Nalini, Vishal M. Patel, Richa Singh e Mayank Vatsa. Domain Adaptation for Visual Understanding. Springer International Publishing AG, 2021.

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20

Csurka, Gabriela. Domain Adaptation in Computer Vision Applications. Springer, 2017.

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21

Redko, Ievgen, Amaury Habrard, Emile Morvant, Marc Sebban e Younes Bennani. Domain Adaptation Theory: Available Theoretical Results. Elsevier, 2019.

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22

Csurka, Gabriela. Domain Adaptation in Computer Vision Applications. Springer, 2018.

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23

Simonson, Tatum S., e Francisco C. Villafuerte, a cura di. Time Domains of Hypoxia Adaptation: Evolutionary Insights and Applications. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-846-7.

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24

Hospedales, Timothy M., Tatiana Tommasi, Gabriela Csurka e Mathieu Salzmann. Visual Domain Adaptation in the Deep Learning Era. Morgan & Claypool Publishers, 2022.

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25

Hospedales, Timothy M., Tatiana Tommasi, Gabriela Csurka e Mathieu Salzmann. Visual Domain Adaptation in the Deep Learning Era. Springer International Publishing AG, 2022.

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26

Hospedales, Timothy M., Tatiana Tommasi, Gabriela Csurka e Mathieu Salzmann. Visual Domain Adaptation in the Deep Learning ERA. Morgan & Claypool Publishers, 2022.

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27

Hospedales, Timothy M., Tatiana Tommasi, Gabriela Csurka e Mathieu Salzmann. Visual Domain Adaptation in the Deep Learning ERA. Morgan & Claypool Publishers, 2022.

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28

Venkateswara, Hemanth, e Sethuraman Panchanathan. Domain Adaptation in Computer Vision with Deep Learning. Springer, 2020.

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29

Venkateswara, Hemanth, e Sethuraman Panchanathan. Domain Adaptation in Computer Vision with Deep Learning. Springer International Publishing AG, 2021.

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30

Hospedales, Timothy M., Tatiana Tommasi, Gabriela Csurka e Mathieu Salzmann. Visual Domain Adaptation in the Deep Learning Era. Springer International Publishing AG, 2022.

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31

Du, Zhekai. Unsupervised Domain Adaptation: Recent Advances and Future Perspectives. Springer, 2024.

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32

Jackson, Scott. Systems Engineering for Commercial Aircraft: A Domain-Specific Adaptation. CRC Press LLC, 2016.

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33

Jackson, Scott. Systems Engineering for Commercial Aircraft: A Domain-Specific Adaptation. CRC Press LLC, 2016.

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34

Jackson, Scott. Systems Engineering for Commercial Aircraft: A Domain-Specific Adaptation. CRC Press LLC, 2020.

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35

Jackson, Scott. Systems engineering for commercial aircraft: A domain-specific adaptation. 2017.

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36

Jackson, Scott. Systems Engineering for Commercial Aircraft: A Domain-Specific Adaptation. CRC Press LLC, 2020.

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37

Jackson, Scott. Systems Engineering for Commercial Aircraft: A Domain-Specific Adaptation. CRC Press LLC, 2020.

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38

Jackson, Scott. Systems Engineering for Commercial Aircraft: A Domain-Specific Adaptation. CRC Press LLC, 2020.

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39

Jackson, Scott. Systems Engineering for Commercial Aircraft: A Domain-Specific Adaptation. CRC Press LLC, 2016.

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40

Jackson, Scott. Systems Engineering for Commercial Aircraft: A Domain-Specific Adaptation. CRC Press LLC, 2016.

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41

Søgaard, Anders. Semi-Supervised Learning and Domain Adaptation in Natural Language Processing. Morgan & Claypool Publishers, 2013.

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42

Søgaard, Anders. Semi-Supervised Learning and Domain Adaptation in Natural Language Processing. Morgan & Claypool Publishers, 2013.

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43

Søgaard, Anders. Semi-Supervised Learning and Domain Adaptation in Natural Language Processing. Springer International Publishing AG, 2013.

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44

Bunt, Harry, Joakim Nivre e Paola Merlo. Trends in Parsing Technology: Dependency Parsing, Domain Adaptation, and Deep Parsing. Springer, 2012.

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45

Termeer, Catrien, Arwin van Buuren, Art Dewulf, Dave Huitema, Heleen Mees, Sander Meijerink e Marleen van Rijswick. Governance Arrangements for Adaptation to Climate Change. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.600.

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Abstract (sommario):

Adaptation to climate change is not only a technical issue; above all, it is a matter of governance. Governance is more than government and includes the totality of interactions in which public as well as private actors participate, aiming to solve societal problems. Adaptation governance poses some specific, demanding challenges, such as the context of institutional fragmentation, as climate change involves almost all policy domains and governance levels; the persistent uncertainties about the nature and scale of risks and proposed solutions; and the need to make short-term policies based on long-term projections. Furthermore, adaptation is an emerging policy field with, at least for the time being, only weakly defined ambitions, responsibilities, procedures, routines, and solutions. Many scholars have already shown that complex problems, such as adaptation to climate change, cannot be solved in a straightforward way with actions taken by a hierarchic or monocentric form of governance. This raises the question of how to develop governance arrangements that contribute to realizing adaptation options and increasing the adaptive capacity of society. A series of seven basic elements have to be addressed in designing climate adaptation governance arrangements: the framing of the problem, the level(s) at which to act, the alignment across sectoral boundaries, the timing of the policies, the selection of policy instruments, the organization of the science-policy interface, and the most appropriate form of leadership. For each of these elements, this chapter suggests some tentative design principles. In addition to effectiveness and legitimacy, resilience is an important criterion for evaluating these arrangements. The development of governance arrangements is always context- and time-specific, and constrained by the formal and informal rules of existing institutions.

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46

Cervantes, Richard C., e Thuy Bui. Redefining the Contexts of Acculturation Related Stress Among Latino Adults. A cura di Seth J. Schwartz e Jennifer Unger. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190215217.013.31.

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The scientific and clinical need to advance understanding of the processes related to Hispanic acculturation and its impacts is pressing. This chapter articulates how acculturation stress and related specific stressor events occur within distinct life domains or contexts. New research is juxtaposed to previous research that demonstrated how acculturation stressors cluster in unique, orthogonal, and independent life domains among both adult and adolescent Hispanics. This chapter refers to contexts of acculturation stress as spheres of life or domains that entail social and psychological interactions with the dominant, receiving culture. New research is presented on the relationship between acculturation stress context among adult Hispanics and mental health indicators. Understanding the contexts in which acculturation-related stress can impact Latinos is critical to health and behavioral health programming, where such information can assist in the development, adaptation, and tailoring of prevention and interventions that are more acceptable and relevant for this growing population.

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47

Hayden, Elizabeth P., e C. Emily Durbin. Development and Psychopathology. A cura di Thomas H. Ollendick, Susan W. White e Bradley A. White. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190634841.013.3.

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The developmental psychopathology perspective, which can be understood as both a conceptual approach and a scientific discipline, aims to integrate the historically distinct domains of child development and psychopathology toward the goal of advancing the understanding of children’s adaptation and maladaptation. This chapter provides an overview of the key concepts and methodologies that characterize the discipline, drawing heavily on seminal early work on the topic; these key concepts and methodologies are integrated with a consideration of current trends and concepts in the field. The current state of the field is summarized and outstanding issues that merit further conceptual consideration and research attention are highlighted.

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48

Zhou, Kevin, Qian Wang, M. Jorge Cardoso, Fausto Milletari, Hien V. Nguyen, Shadi Albarqouni, Nicola Rieke et al. Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data: First MICCAI Workshop, DART 2019, and ... Springer, 2019.

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49

Islam, Mobarakol, Jorge Cardoso, Lisa Koch, Ziyue Xu e Konstantinos Kamnitsas. Domain Adaptation and Representation Transfer: 4th MICCAI Workshop, DART 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Springer, 2022.

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50

Jiang, Meirui. Domain Adaptation and Representation Transfer: 5th MICCAI Workshop, DART 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings. Springer, 2023.

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