Sijia liu (26 resultados)

Condición: As New. Unread book in perfect condition. Li, Sijia; Liu, Fudong (ilustrador).

Condición: New. Li, Sijia; Liu, Fudong (ilustrador).

Hardback. Condición: New. Li, Sijia; Liu, Fudong (ilustrador).

Hardback. Condición: New. Li, Sijia; Liu, Fudong (ilustrador).

Condición: New. Li, Sijia; Liu, Fudong (ilustrador).

Condición: New. Li, Sijia; Liu, Fudong (ilustrador).

Buch. Condición: Neu. Neuware - The SEE Noah's Ark Biodiversity Conservation Book Series consists of five short-story picture books on endangered animal species in Southwest China. Each volume focuses on a different animal, following the path of a particular creature on its quest for survival. The series is part of the Southwest Project Center of the Alashan Society of Entrepreneurs & Ecology (SEE), one of the first NGOs set up in China, back in 2004. The stories are based on the real-life experiences of front-line rangers and locals in Southwest China, and can be fun and educational for young readers. This volume follows the journey of a young Himalayan honeybee - Little Ya. Little Ya is a worker bee in a large bee hive which is soon upended by human activity, creating a great disturbance . . . Will the problem be solved The Himalayan honeybee tends to be found in mountainous regions in northwest Yunnan Province. Li, Sijia; Liu, Fudong (ilustrador).…

Hardback. Condición: New. Li, Sijia; Liu, Fudong (ilustrador).

Gebunden. Condición: New. Li, Sijia; Liu, Fudong (ilustrador).

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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a systematic and in-depth introduction to machine unlearning (MU) for foundation models, framed through an optimization model data tri-design perspective and complemented by assessments and applications. As foundation models are continuously adapted and reused, the ability to selectively remove unwanted data, knowledge, or model behavior, without full retraining, poses new theoretical and practical challenges. Thus, MU has become a critical capability for trustworthy, deployable, and regulation-ready artificial intelligence. From the optimization viewpoint, this book treats unlearning as a multi-objective and often adversarial problem that must simultaneously enforce targeted forgetting, preserve model utility, resist recovery attacks, and remain computationally efficient. From the model perspective, the book examines how knowledge is distributed across layers and latent subspaces, motivating modular and localized unlearning. From the data perspective, the book explores forget-set construction, data attribution, corruption, and coresets as key drivers of reliable forgetting.Bridging theory and practice, the book also provides a comprehensive review of benchmark datasets and evaluation metrics for machine unlearning, critically examining their strengths and limitations. The authors further survey a wide range of applications in computer vision and large language models, including AI safety, privacy, fairness, and industrial deployment, highlighting why post-training model modification is often preferred over repeated retraining in real-world systems. By unifying optimization, model, data, evaluation, and application perspectives, this book offers both a foundational framework and a practical toolkit for designing machine unlearning methods that are effective, robust, and ready for large-scale, regulated deployment.…

Condición: As New. Unread book in perfect condition.

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Hardback. Condición: New. Li, Sijia; Liu, Fudong (ilustrador).

Condición: As New. Unread book in perfect condition.

Hardcover. Condición: Brand New. 323 pages. 9.26x6.11x9.21 inches. In Stock.

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a systematic and in-depth introduction to machine unlearning (MU) for foundation models, framed through an optimization model data tri-design perspective and complemented by assessments and applications. As foundation models are continuously adapted and reused, the ability to selectively remove unwanted data, knowledge, or model behavior, without full retraining, poses new theoretical and practical challenges. Thus, MU has become a critical capability for trustworthy, deployable, and regulation-ready artificial intelligence. From the optimization viewpoint, this book treats unlearning as a multi-objective and often adversarial problem that must simultaneously enforce targeted forgetting, preserve model utility, resist recovery attacks, and remain computationally efficient. From the model perspective, the book examines how knowledge is distributed across layers and latent subspaces, motivating modular and localized unlearning. From the data perspective, the book explores forget-set construction, data attribution, corruption, and coresets as key drivers of reliable forgetting.Bridging theory and practice, the book also provides a comprehensive review of benchmark datasets and evaluation metrics for machine unlearning, critically examining their strengths and limitations. The authors further survey a wide range of applications in computer vision and large language models, including AI safety, privacy, fairness, and industrial deployment, highlighting why post-training model modification is often preferred over repeated retraining in real-world systems. By unifying optimization, model, data, evaluation, and application perspectives, this book offers both a foundational framework and a practical toolkit for designing machine unlearning methods that are effective, robust, and ready for large-scale, regulated deployment. 264 pp. Englisch.…

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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book provides a systematic and in-depth introduction to machine unlearning (MU) for foundation models, framed through an optimization-model-data tri-design perspective and complemented by assessments and applications. As foundation models are continuously adapted and reused, the ability to selectively remove unwanted data, knowledge, or model behavior, without full retraining, poses new theoretical and practical challenges. Thus, MU has become a critical capability for trustworthy, deployable, and regulation-ready artificial intelligence. From the optimization viewpoint, this book treats unlearning as a multi-objective and often adversarial problem that must simultaneously enforce targeted forgetting, preserve model utility, resist recovery attacks, and remain computationally efficient. From the model perspective, the book examines how knowledge is distributed across layers and latent subspaces, motivating modular and localized unlearning. From the data perspective, the book explores forget-set construction, data attribution, corruption, and coresets as key drivers of reliable forgetting. Bridging theory and practice, the book also provides a comprehensive review of benchmark datasets and evaluation metrics for machine unlearning, critically examining their strengths and limitations. The authors further survey a wide range of applications in computer vision and large language models, including AI safety, privacy, fairness, and industrial deployment, highlighting why post-training model modification is often preferred over repeated retraining in real-world systems. By unifying optimization, model, data, evaluation, and application perspectives, this book offers both a foundational framework and a practical toolkit for designing machine unlearning methods that are effective, robust, and ready for large-scale, regulated deployment.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 276 pp. Englisch.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book offers an extensive exploration of foundation models, guiding readers through the essential concepts and advanced topics that define this rapidly evolving research area. Designed for those seeking to deepen their understanding and contribute to the development of safer and more trustworthy AI technologies, the book is divided into three parts providing the fundamentals, advanced topics in foundation modes, and safety and trust in foundation models: Part Iintroduces the core principles of foundation models and generative AI, presents the technical background of neural networks, delves into the learning and generalization of transformers, and finishes withthe intricacies of transformers and in-context learning. Part IIintroduces automated visual prompting techniques,prompting LLMs with privacy,memory-efficient fine-tuning methods, and shows how LLMs can be reprogrammed for time-series machine learning tasks. It explores how LLMs can be reused for speech tasks, how synthetic datasets can be used tobenchmark foundation models, and elucidates machine unlearning for foundation models. Part IIIprovides a comprehensive evaluation of the trustworthiness of LLMs, introduces jailbreak attacks and defenses for LLMs, presents safety risks when find-tuning LLMs, introduces watermarking techniques for LLMs, presents robust detection of AI-generated text, elucidates backdoor risks in diffusion models, and presents red-teaming methods for diffusion models. Mathematical notations are clearly defined and explained throughout, making this book an invaluable resource for both newcomers and seasoned researchers in the field.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 324 pp. Englisch.…