Verlag: Springer International Publishing, 2021
ISBN 10: 3030666441 ISBN 13: 9783030666446
Sprache: Englisch
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In den WarenkorbZustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher.
Verlag: Springer International Publishing, Springer Nature Switzerland Jan 2021, 2021
ISBN 10: 3030666441 ISBN 13: 9783030666446
Sprache: Englisch
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In den WarenkorbTaschenbuch. Zustand: Neu. Neuware -This book constitutes the proceedings of the 13th International Conference on Intelligent Robotics and Applications, ICIRA 2020, held in Kuala Lumpur, Malaysia, in November 2020.The 45 full papers and 3 short papers were carefully reviewed and selected from 66 submissions. The accepted papers were grouped into various subtopics including Advanced Measurement and Machine Vision System; Automation; Human-Robot Interaction; Mobile Robots and Intelligent Autonomous System; Recent Trends in Computational Intelligence; Robot Design, and Development and Control.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 560 pp. Englisch.
Verlag: Springer International Publishing, 2021
ISBN 10: 3030666441 ISBN 13: 9783030666446
Sprache: Englisch
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In den WarenkorbTaschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the proceedings of the 13th International Conference on Intelligent Robotics and Applications, ICIRA 2020, held in Kuala Lumpur, Malaysia, in November 2020. The 45 full papers and 3 short papers were carefully reviewed and selected from 66 submissions. The accepted papers were grouped into various subtopics including Advanced Measurement and Machine Vision System; Automation; Human-Robot Interaction; Mobile Robots and Intelligent Autonomous System; Recent Trends in Computational Intelligence; Robot Design, and Development and Control.Due to the Corona pandemic ICIRA 2020 was held as a virtual event.
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In den WarenkorbZustand: New. In.
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In den WarenkorbZustand: New. In.
Verlag: Springer-Nature New York Inc, 2021
ISBN 10: 3030666441 ISBN 13: 9783030666446
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: Brand New. 528 pages. 9.25x6.10x1.14 inches. In Stock.
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In den WarenkorbZustand: New.
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In den WarenkorbZustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
Verlag: Springer Berlin Heidelberg, 2018
ISBN 10: 3662571528 ISBN 13: 9783662571521
Sprache: Englisch
Anbieter: moluna, Greven, Deutschland
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In den WarenkorbZustand: New.
Verlag: Springer Berlin Heidelberg, 2017
ISBN 10: 3662536900 ISBN 13: 9783662536902
Sprache: Englisch
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In den WarenkorbGebunden. Zustand: New.
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In den WarenkorbZustand: New.
Verlag: Springer, Berlin|Springer Nature Singapore|Springer, 2024
ISBN 10: 9811975566 ISBN 13: 9789811975561
Sprache: Englisch
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In den WarenkorbZustand: New.
Verlag: Springer, Berlin|Springer Nature Singapore|Springer, 2023
ISBN 10: 9811975531 ISBN 13: 9789811975530
Sprache: Englisch
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In den WarenkorbZustand: New.
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In den WarenkorbZustand: New. In.
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In den WarenkorbZustand: New. In.
Verlag: Springer Nature Singapore, Springer Nature Singapore Mai 2024, 2024
ISBN 10: 9811975566 ISBN 13: 9789811975561
Sprache: Englisch
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In den WarenkorbTaschenbuch. Zustand: Neu. Neuware -Machine learning (ML) models, especially large pretrained deep learning (DL) models, are of high economic value and must be properly protected with regard to intellectual property rights (IPR). Model watermarking methods are proposed to embed watermarks into the target model, so that, in the event it is stolen, the model¿s owner can extract the pre-defined watermarks to assert ownership. Model watermarking methods adopt frequently used techniques like backdoor training, multi-task learning, decision boundary analysis etc. to generate secret conditions that constitute model watermarks or fingerprints only known to model owners. These methods have little or no effect on model performance, which makes them applicable to a wide variety of contexts. In terms of robustness, embedded watermarks must be robustly detectable against varying adversarial attacks that attempt to remove the watermarks. The efficacy of model watermarking methods is showcased in diverse applications including image classification, image generation, image captions, natural language processing and reinforcement learning.This book covers the motivations, fundamentals, techniques and protocols for protecting ML models using watermarking. Furthermore, it showcases cutting-edge work in e.g. model watermarking, signature and passport embedding and their use cases in distributed federated learning settings.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 244 pp. Englisch.
Verlag: Springer Nature Singapore, Springer Nature Singapore Mai 2023, 2023
ISBN 10: 9811975531 ISBN 13: 9789811975530
Sprache: Englisch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
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In den WarenkorbBuch. Zustand: Neu. Neuware -Machine learning (ML) models, especially large pretrained deep learning (DL) models, are of high economic value and must be properly protected with regard to intellectual property rights (IPR). Model watermarking methods are proposed to embed watermarks into the target model, so that, in the event it is stolen, the model¿s owner can extract the pre-defined watermarks to assert ownership. Model watermarking methods adopt frequently used techniques like backdoor training, multi-task learning, decision boundary analysis etc. to generate secret conditions that constitute model watermarks or fingerprints only known to model owners. These methods have little or no effect on model performance, which makes them applicable to a wide variety of contexts. In terms of robustness, embedded watermarks must be robustly detectable against varying adversarial attacks that attempt to remove the watermarks. The efficacy of model watermarking methods is showcased in diverse applications including image classification, image generation, image captions, natural language processing and reinforcement learning.This book covers the motivations, fundamentals, techniques and protocols for protecting ML models using watermarking. Furthermore, it showcases cutting-edge work in e.g. model watermarking, signature and passport embedding and their use cases in distributed federated learning settings.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 244 pp. Englisch.
Verlag: Springer Nature Singapore, Springer Nature Singapore, 2024
ISBN 10: 9811975566 ISBN 13: 9789811975561
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
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In den WarenkorbTaschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning (ML) models, especially large pretrained deep learning (DL) models, are of high economic value and must be properly protected with regard to intellectual property rights (IPR). Model watermarking methods are proposed to embed watermarks into the target model, so that, in the event it is stolen, the model's owner can extract the pre-defined watermarks to assert ownership. Model watermarking methods adopt frequently used techniques like backdoor training, multi-task learning, decision boundary analysis etc. to generate secret conditions that constitute model watermarks or fingerprints only known to model owners. These methods have little or no effect on model performance, which makes them applicable to a wide variety of contexts. In terms of robustness, embedded watermarks must be robustly detectable against varying adversarial attacks that attempt to remove the watermarks. The efficacy of model watermarking methods is showcased in diverse applications including image classification, image generation, image captions, natural language processing and reinforcement learning. This book covers the motivations, fundamentals, techniques and protocols for protecting ML models using watermarking. Furthermore, it showcases cutting-edge work in e.g. model watermarking, signature and passport embedding and their use cases in distributed federated learning settings.
Verlag: Springer Nature Singapore, Springer Nature Singapore, 2023
ISBN 10: 9811975531 ISBN 13: 9789811975530
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 175,09
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In den WarenkorbBuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning (ML) models, especially large pretrained deep learning (DL) models, are of high economic value and must be properly protected with regard to intellectual property rights (IPR). Model watermarking methods are proposed to embed watermarks into the target model, so that, in the event it is stolen, the model's owner can extract the pre-defined watermarks to assert ownership. Model watermarking methods adopt frequently used techniques like backdoor training, multi-task learning, decision boundary analysis etc. to generate secret conditions that constitute model watermarks or fingerprints only known to model owners. These methods have little or no effect on model performance, which makes them applicable to a wide variety of contexts. In terms of robustness, embedded watermarks must be robustly detectable against varying adversarial attacks that attempt to remove the watermarks. The efficacy of model watermarking methods is showcased in diverse applications including image classification, image generation, image captions, natural language processing and reinforcement learning. This book covers the motivations, fundamentals, techniques and protocols for protecting ML models using watermarking. Furthermore, it showcases cutting-edge work in e.g. model watermarking, signature and passport embedding and their use cases in distributed federated learning settings.
Anbieter: Kennys Bookstore, Olney, MD, USA
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In den WarenkorbZustand: New. 2018. Paperback. . . . . . Books ship from the US and Ireland.
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In den WarenkorbHardcover. Zustand: Brand New. 281 pages. 9.50x6.25x1.00 inches. In Stock.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
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In den WarenkorbHardcover. Zustand: Brand New. 241 pages. 9.25x6.10x0.79 inches. In Stock.
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In den WarenkorbZustand: New. 2023. 1st ed. 2023. hardcover. . . . . . Books ship from the US and Ireland.
Verlag: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 2017
ISBN 10: 3662536900 ISBN 13: 9783662536902
Sprache: Englisch
Anbieter: Kennys Bookstore, Olney, MD, USA
EUR 261,16
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In den WarenkorbZustand: New. Series: Studies in Computational Intelligence. Num Pages: 250 pages, 100 colour illustrations, biography. BIC Classification: MFG; MQW; UYQ. Category: (P) Professional & Vocational. Dimension: 235 x 155. . . 2017. Hardback. . . . . Books ship from the US and Ireland.