Hardware Aware Probabilistic Machine Learning by Galindez Olascoaga (25 results)
Hardware-aware Probabilistic Machine Learning Models : Learning, Inference and Use Cases
Olascoaga, Laura Isabel Galindez; Meert, Wannes; Verhelst, Marian
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Hardware-aware Probabilistic Machine Learning Models : Learning, Inference and Use Cases
Olascoaga, Laura Isabel Galindez; Meert, Wannes; Verhelst, Marian
- Softcover
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Hardware-Aware Probabilistic Machine Learning Models: Learning, Inference and Use Cases
Galindez Olascoaga, Laura Isabel; Meert, Wannes; Verhelst, Marian
- Softcover
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Hardware-aware Probabilistic Machine Learning Models : Learning, Inference and Use Cases
Olascoaga, Laura Isabel Galindez; Meert, Wannes; Verhelst, Marian
- Softcover
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Hardware-Aware Probabilistic Machine Learning Models: Learning, Inference and Use Cases
Galindez Olascoaga, Laura Isabel; Meert, Wannes; Verhelst, Marian
- Softcover
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Condition: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.
Hardware-aware Probabilistic Machine Learning Models : Learning, Inference and Use Cases
Olascoaga, Laura Isabel Galindez; Meert, Wannes; Verhelst, Marian
- Softcover
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Hardware-aware Probabilistic Machine Learning Models : Learning, Inference and Use Cases
Olascoaga, Laura Isabel Galindez; Meert, Wannes; Verhelst, Marian
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Hardware-Aware Probabilistic Machine Learning Models: Learning, Inference and Use Cases
Galindez Olascoaga, Laura Isabel; Meert, Wannes; Verhelst, Marian
- Hardcover
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Hardware-aware Probabilistic Machine Learning Models : Learning, Inference and Use Cases
Olascoaga, Laura Isabel Galindez; Meert, Wannes; Verhelst, Marian
- Hardcover
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Hardware-aware Probabilistic Machine Learning Models : Learning, Inference and Use Cases
Olascoaga, Laura Isabel Galindez; Meert, Wannes; Verhelst, Marian
- Hardcover
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Hardware-aware Probabilistic Machine Learning Models : Learning, Inference and Use Cases
Olascoaga, Laura Isabel Galindez; Meert, Wannes; Verhelst, Marian
- Hardcover
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- Softcover
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and pe…rformance of the machine learning task, with the overarching goal of balancing the two optimally. The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover.The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering.
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Taschenbuch. Condition: Neu. Hardware-Aware Probabilistic Machine Learning Models | Learning, Inference and Use Cases | Laura Isabel Galindez Olascoaga (u. a.) | Taschenbuch | xii | Englisch | 2022 | Springer | EAN 9783030740443 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juerg…en[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Hardware-Aware Probabilistic Machine Learning Models: Learning, Inference and Use Cases
Galindez Olascoaga, Laura Isabel (Author)/ Meert, Wannes (Author)/ Verhelst, Marian (Author)
- Hardcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Hardcover. Condition: Brand New. 175 pages. 9.25x6.10x0.59 inches. In Stock.
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and performan…ce of the machine learning task, with the overarching goal of balancing the two optimally. The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover.The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering.
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- Hardcover
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Language: English
Published by Springer International Publishing Mai 2022, 2022
- Softcover
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource co…nsumption and performance of the machine learning task, with the overarching goal of balancing the two optimally. The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover.The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering. 176 pp. Englisch.
Hardware-Aware Probabilistic Machine Learning Models: Learning, Inference and Use Cases
Galindez Olascoaga, Laura Isabel; Meert, Wannes; Verhelst, Marian
- Softcover
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Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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Hardware-Aware Probabilistic Machine Learning Models: Learning, Inference and Use Cases
Galindez Olascoaga, Laura Isabel; Meert, Wannes; Verhelst, Marian
- Softcover
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Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Hardware-Aware Probabilistic Machine Learning Models
Galindez Olascoaga, Laura Isabel|Meert, Wannes|Verhelst, Marian
Language: English
Published by Springer, Berlin|Springer International Publishing|Springer, 2022
- Softcover
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Seller: moluna, Greven, Germanymoluna
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have… on resource consumption and performa.
Language: English
Published by Springer International Publishing Mai 2021, 2021
- Hardcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumpti…on and performance of the machine learning task, with the overarching goal of balancing the two optimally. The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover.The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering. 176 pp. Englisch.
Hardware-Aware Probabilistic Machine Learning Models
Laura Isabel Galindez Olascoaga|Wannes Meert|Marian Verhelst
- Hardcover
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Seller: moluna, Greven, Germanymoluna
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Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Introduces a new, systematic approach for the realization of hardware-awareness with probabilistic modelsEnables readers to accommodate various systems and applications, as demonstrated with multiple use cas…es targeting distinct types of device.
- Softcover
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Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consum…ption and performance of the machine learning task, with the overarching goal of balancing the two optimally.The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover.The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 176 pp. Englisch.
- Hardcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption a…nd performance of the machine learning task, with the overarching goal of balancing the two optimally.The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover.The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 176 pp. Englisch.
