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Livres sur le sujet « Representation learning (artifical intelligence) »

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Auteur : Grafiati

Publié le 7 juillet 2024

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1

Pacific, Rim International Conference on Artificial Intelligence (4th 1996 Cairns Qld ). PRICAI '96 : Topics in artificial intelligence : 4th Pacific Rim International Conference on Artificial Intelligence, Cairns, Australia, August 26-30, 1996 : proceedings. Berlin : Springer, 1996.

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2

Brighton, England) International Conference on Artificial Intelligence in Education (14th 2009. Artificial intelligence in education : Building learning systems that care : from knowledge representation to affective modelling. Amsterdam : IOS Press, 2009.

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3

1953-, Benjamin D. Paul, dir. Change of representation and inductive bias. Boston : Kluwer Academic, 1990.

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4

Andrée, Tiberghien, Mandl Heinz et NATO Advanced Research Workshop on Knowledge Acquisition in the Domain of Physics and Intelligent Learning Environments (1990 : Lyon, France), dir. Intelligent learning environments and knowledge acquisition in physics. Berlin : Springer-Verlag, 1992.

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5

Tiberghien, Andrée. Intelligent Learning Environments and Knowledge Acquisition in Physics. Berlin, Heidelberg : Springer Berlin Heidelberg, 1992.

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6

Workshop on Reasoning with Incomplete and Changing Information (1996 Cairns, Qld.). Learning and reasoning with complex representations : PRICAI'96 Workshops on Reasoning with Incomplete and Changing Information and on Inducing Complex Representations, Cairns, Australia, August 26-30, 1996 : selected papers. Berlin : Springer, 1998.

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7

Fisseler, Jens. Learning and modeling with probabilistic conditional logic. Heidelberg : Ios Press, 2010.

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8

KR4HC 2009 (2009 Verona, Italy). Knowledge representation for health-care : Data, processes and guidelines : AIME 2009 workshop KR4HC 2009, Verona, Italy, July 19, 2009 : revised selected papers. Berlin : Springer, 2010.

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9

Riaño, David. Knowledge Representation for Health-Care : ECAI 2010 Workshop KR4HC 2010, Lisbon, Portugal, August 17, 2010, Revised Selected Papers. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011.

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10

United States. National Aeronautics and Space Administration., dir. Instructable autonomous agents : CSE-TR-193-94. [Washington, DC : National Aeronautics and Space Administration, 1994.

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11

Bareiss, Ray. Exemplar-based knowledge acquisition : A unified approach to concept representation, classification, and learning. Boston : Academic Press, 1989.

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12

Lin, Yankai, Zhiyuan Liu et Maosong Sun. Representation Learning for Natural Language Processing. Springer Singapore Pte. Limited, 2020.

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13

Liu, Zhiyuan. Representation Learning for Natural Language Processing. Springer Nature, 2020.

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14

Lin, Yankai, Zhiyuan Liu et Maosong Sun. Representation Learning for Natural Language Processing. Springer Singapore Pte. Limited, 2020.

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15

Di Maio, Paolad. Knowledge Representation for Debiasing. MTS Press, 2021. http://dx.doi.org/10.52844/slkrns1.

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Bias in Artificial Intelligence and Machine Learning is a top concern for developers and users of intelligent technology. Intelligent systems can, in theory, be designed to identify and resolve algorithmic bias, but for this to be possible it is necessary to have an adequate knowledge representation of bias itself, which has never been fully achieved. Here a KR of bias is presented as a matrix and a relational set constituting the logical and functional foundation of an intelligent system capable of autonomously identifying, measuring and minimise algorithmic bias.

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16

(Editor), Norman Foo, et Randy Goebel (Editor), dir. Pricai '96 : Topics in Artificial Intelligence : 4th Pacific Rim International Conference on Artificial Intelligence, Cairns, Australia, August 26-30, 1996 : Proce (Lecture Notes in Computer Science). Springer, 1996.

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17

Gou, Jianping, Weihua Ou, Shaoning Zeng et Lan Du, dir. Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics. MDPI, 2023. http://dx.doi.org/10.3390/books978-3-0365-7263-5.

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18

Benjamin, D. Paul. Change of Representation and Inductive Bias. Springer, 2011.

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19

Boden, Margaret A. 2. General intelligence as the Holy Grail. Oxford University Press, 2018. http://dx.doi.org/10.1093/actrade/9780199602919.003.0002.

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A host of state-of-the-art AI applications exist, designed for countless specific tasks and used in almost every area of life, by laymen and professionals alike. Many outperform even the most expert humans. In that sense, progress has been spectacular. But the AI pioneers were also hoping for systems with general intelligence. ‘General intelligence as the Holy Grail’ explains why artificial general intelligence is still highly elusive despite recent increases in computer power. It considers the general AI strategies in recent research—heuristics, planning, mathematical simplification, and different forms of knowledge representation—and discusses the concepts of the frame problem, agents and distributed cognition, machine learning, and generalist systems.

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20

(Editor), Jonathan Lawry, Jimi Shanahan (Editor) et Anca Ralescu (Editor), dir. Modelling with Words : Learning, Fusion, and Reasoning within a Formal Linguistic Representation Framework (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence). Springer, 2004.

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21

Domingos, Pedro. Master Algorithm : How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books, 2015.

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22

Domingos, Pedro. Master Algorithm : How the Quest for the Ultimate Learning Machine Will Remake Our World. Penguin Books, Limited, 2015.

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23

Domingos, Pedro. Master Algorithm : How the Quest for the Ultimate Learning Machine Will Remake Our World. Penguin Books, Limited, 2017.

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24

Domingos, Pedro. The Master Algorithm : How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books, 2018.

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25

Domingos, Pedro. The master algorithm : How the quest for the ultimate learning machine will remake our world. 2015.

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26

L'algoritmo definitivo : La macchina che impara da sola e il futuro del nostro mondo. Bollati Boringhieri, 2016.

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27

Domingos, Pedro. Verkhovnyĭ algoritm : Kak mashinnoe obuchenie izmenit nash mir. 2016.

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28

Jackson, Stuart A. Connectionism and Vermont : From Truth Conditions to Weight Representations (Albex Series in Artificial Intelligence). Ablex Publishing Corporation, 1997.

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29

Jackson, Stuart A. Connectionism and Meaning : From Truth Conditions to Weight Representations (Ablex Series in Artificial Intelligence). Ablex Publishing Corporation, 1997.

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30

(Editor), Ulrich Hoppe, Felisa Verdejo (Editor) et Judy Kay (Editor), dir. Artifical Intelligence in Education : Shaping the Future of Learning Through Intelligent Technologies (Frontiers in Artificial Intelligence and Applications,). IOS Press, 2003.

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31

Ward, Matt, et Bernard Marr. Artifical Intelligence in Practice : How 50 Successful Companies Used AI and Machine Learning to Solve Problems. Wiley & Sons, Limited, John, 2019.

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32

Caselli, Tommaso, Eduard Hovy, Martha Palmer et Piek Vossen, dir. Computational Analysis of Storylines. Cambridge University Press, 2021. http://dx.doi.org/10.1017/9781108854221.

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Event structures are central in Linguistics and Artificial Intelligence research: people can easily refer to changes in the world, identify their participants, distinguish relevant information, and have expectations of what can happen next. Part of this process is based on mechanisms similar to narratives, which are at the heart of information sharing. But it remains difficult to automatically detect events or automatically construct stories from such event representations. This book explores how to handle today's massive news streams and provides multidimensional, multimodal, and distributed approaches, like automated deep learning, to capture events and narrative structures involved in a 'story'. This overview of the current state-of-the-art on event extraction, temporal and casual relations, and storyline extraction aims to establish a new multidisciplinary research community with a common terminology and research agenda. Graduate students and researchers in natural language processing, computational linguistics, and media studies will benefit from this book.

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33

Leondes, Cornelius T. Knowledge-Based Systems Techniques and Applications (4-Volume Set). Academic Press, 2000.

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34

Leondes, Cornelius T. Knowledge-Based Systems Techniques and Applications (4-Volume Set). Academic Press, 2000.

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35

Bareiss, Ray, et B. Chandrasekaran. Exemplar-Based Knowledge Acquisition : A Unified Approach to Concept Representation, Classification, and Learning. Elsevier Science & Technology Books, 2014.

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36

The Expected Knowledge : What can we know about anything and everything ? Tiruchirappalli : Sivashanmugam Palaniappan, 2012.

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37

Bareiss, Ray. Exemplar Based Knowledge Acquisition : A Unified Approach to Concept Representation Classification, and Learning (Prspctvs in Art Intlgnce, Vol 2). Academic Pr, 1990.

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38

Bareiss, Ray. Exemplar Based Knowledge Acquisition : A Unified Approach to Concept Representation Classification, and Learning (Prspctvs in Art Intlgnce, Vol 2). Academic Pr, 1990.

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