Qingsong wu (57 Ergebnisse)

Softcover. Zustand: Bon. Couverture différente. Tome 1. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Different cover. Volume 1. Ammareal gives back up to 15% of this item's net price to charity organizations.

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Zustand: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages. Qingsong, Wu (illustrator).

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

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

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ÉTAT : NEUF / NEW CONDITION 9782413038078 714505 6.

Paperback. Zustand: Brand New. 188 pages. German language. 7.68x0.67x10.94 inches. In Stock.

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Paperback. Zustand: Brand New. 234 pages. 9.18x6.12x9.21 inches. In Stock.

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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.…

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), .

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Zustand: Bon. Attention: Ancien support de bibliothèque, plastifié, étiquettes. Merci, votre achat aide à financer des programmes de lutte contre l'illettrisme.

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.…

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.

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.…

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Intense uplift of the Tibetan Plateau in Late Cenozoic Era is one of the most important events in geological history of the Earth. The plateau offers an ideal region for studying of lithospheric formation and evolution, probing into the mechanism of crustal movement, and understanding of changes in environments and geo-ecosystems in Asia. Intense uplift ofthe plateau resulted in drastic changes of natural environment and apparent regional differentiation on the plateau proper and neighboring regions. The plateau therefore becomes a sensitive area of climate change in Asian monsoon region, which is closely related to the global change. As a special physical unit, its ecosystems occupy a prominent position in the world. Due to its extremely high elevation and great extent, natural types and characteristics of physical landscapes on the plateau are quite different from those in lowlands at comparable latitudes, and environments are also different from those in high latitudinal zones. Consequently, the Tibetan Plateau has been classified as one of three giant physical regions in China and considered as a unique unit on Earth. Scientific surveys and expeditions to the Tibetan Plateau on large scale began from 1950's. Amongst them, a number of comprehensive scientific expeditions to the Xizang (Tibet) Autonomous Region, Hengduan Mts. areas, Karakorum and Kunlun Mts. regions, as well as the Hoh Xii Mts. areas, have been successively carried out by the Integrated Scientific Expedition to Tibetan Plateau, sponsored by Chinese Academy of Sciences since 1973. …

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.…