chen xiaoli (27 Ergebnisse)

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

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Paperback. Zustand: Brand New. 9.18x6.12 inches. In Stock.

Paperback. Zustand: Brand New. 151 pages. 9.25x6.10x0.43 inches. In Stock.

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Paperback. Zustand: Brand New. 9.18x6.12 inches. In Stock.

Zustand: NEW.

Paperback. Zustand: Brand New. 246 pages. 9.18x6.12x9.21 inches. In Stock.

Zustand: New. Dr. Min Wu is currently a Principal Scientist at Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore. He received his Ph.D. degree in Computer Science from Nanyang Technological University (NTU), .

Taschenbuch. Zustand: Neu. Deep Learning for Human Activity Recognition | Second International Workshop, DL-HAR 2020, Held in Conjunction with IJCAI-PRICAI 2020, Kyoto, Japan, January 8, 2021, Proceedings | Xiaoli Li (u. a.) | Taschenbuch | xii | Englisch | 2021 | Springer | EAN 9789811605741 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes refereed proceedings of theSecond International Workshop on Deep Learning for Human Activity Recognition, DL-HAR 2020, held in conjunction with IJCAI-PRICAI 2020, in Kyoto, Japan, in January 2021. Due to the COVID-19 pandemic the workshop was postponed to the year 2021 and held in a virtual format.The 10 presented papers were thorougly reviewed and included in the volume. They present recent research on applications of human activity recognition for various areas such as healthcare services, smart home applications, and more.

Zustand: New. Min Wu is currently a Principal Scientist at Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore.Emadeldeen Eldele is an Assistant Professor at Khalifa University, UAE.Zhen.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book constitutes refereed proceedings of the Second International Workshop on Deep Learning for Human Activity Recognition, DL-HAR 2020, held in conjunction with IJCAI-PRICAI 2020, in Kyoto, Japan, in January 2021. Due to the COVID-19 pandemic the workshop was postponed to the year 2021 and held in a virtual format. The 10 presented papers were thorougly reviewed and included in the volume. They present recent research on applications of human activity recognition for various areas such as healthcare services, smart home applications, and more.

Taschenbuch. Zustand: Neu. Neuware - This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advance algorithms that are transforming time series analysis across industries. The authors highlight the use AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time. In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis.TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through unsupervised domain adaptation (UDA) In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like MOIRAI and Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as a supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, climate.

Taschenbuch. Zustand: Neu. Neuware - This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift, and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advanced algorithms that are transforming time series analysis across industries. The authors highlight the use of AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time.In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis. TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through Unsupervised Domain Adaptation (UDA). In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, and climate.

Taschenbuch. Zustand: Neu. AI for Time Series | Volume 2: Building Robust and Generalizable Models | Emadeldeen Eldele (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2026 | Taylor & Francis Ltd | EAN 9781041011033 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Taschenbuch. Zustand: Neu. AI for Time Series | Volume 1: Unlocking Patterns with Deep Learning | Emadeldeen Eldele (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2026 | Taylor & Francis Ltd | EAN 9781041010319 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Hardcover. Zustand: Brand New. 200 pages. 9.61x6.69x0.75 inches. In Stock.

Zustand: New.

Zustand: New.

Zustand: New. Dr. Min Wu is currently a Principal Scientist at Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore. He received his Ph.D. degree in Computer Science from Nanyang Technological University (NTU), .

Zustand: New. Min Wu is currently a Principal Scientist at Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore.Emadeldeen Eldele is an Assistant Professor at Khalifa University, UAE.Zhen.

Buch. Zustand: Neu. Neuware - This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advance algorithms that are transforming time series analysis across industries. The authors highlight the use AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time. In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis.TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through unsupervised domain adaptation (UDA) In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like MOIRAI and Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as a supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, climate.

Buch. Zustand: Neu. Neuware - This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift, and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advanced algorithms that are transforming time series analysis across industries. The authors highlight the use of AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time.In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis. TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through Unsupervised Domain Adaptation (UDA). In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, and climate.

Hardcover. Zustand: Brand New. 246 pages. 9.18x6.12x9.45 inches. In Stock.

129 S. zahlr. Ill. Gr.8°, OKart. Aus dem Chinesischen übers. (= Chinesische Volkskunst). - Leicht bestoßen, sonst guter Zustand. 600 gr.