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In den WarenkorbPaperback. Zustand: Brand New. 370 pages. 11.69x8.25x11.69 inches. In Stock.
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In den WarenkorbZustand: New.
Sprache: Englisch
Verlag: The Institution of Engineering and Technology, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
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In den WarenkorbZustand: New. In.
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In den WarenkorbZustand: New. In.
Sprache: Englisch
Verlag: Inst of Engineering & Technology, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
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In den WarenkorbHardcover. Zustand: Brand New. 1st edition. 400 pages. 9.25x6.25x1.25 inches. In Stock.
Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 397 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
Sprache: Englisch
Verlag: INSTITUTION OF ENGINEERING & T, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
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In den WarenkorbZustand: New. KlappentextThis edited book focuses on the applications of machine learning in the healthcare sector, both at the macro-level for guiding policy decisions, and at the granular level, showing how machine learning techniques can be appl.
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EUR 236,72
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In den WarenkorbZustand: New.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. 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: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. 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.
Sprache: Englisch
Verlag: Institution Of Engineering & Technology Jun 2023, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Neuware - This edited book focuses on the applications of machine learning in the healthcare sector, both at the macro-level for guiding policy decisions, and at the granular level, showing how machine learning techniques can be applied to help individual patients.
Anbieter: preigu, Osnabrück, Deutschland
Buch. Zustand: Neu. Digital Twins | Internet of Things, Machine Learning, and Smart Manufacturing, Smart Computing Applications 8 | Yogini/Borkar, Pradnya/Raut, Roshani et al Borole | Buch | XVI | Englisch | 2023 | De Gruyter GmbH | EAN 9783110778786 | Verantwortliche Person für die EU: Walter de Gruyter GmbH, De Gruyter GmbH, Genthiner Str. 13, 10785 Berlin, productsafety[at]degruyterbrill[dot]com | Anbieter: preigu.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 269,70
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In den WarenkorbHardcover. Zustand: Brand New. 241 pages. 9.25x6.10x0.79 inches. In Stock.