Wang Guangyu (64 results)

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

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

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

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

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

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

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Hardback or Cased Book. Condition: New. Soundscape in Urban Forests. Book.

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Taschenbuch. Condition: Neu. Neuware - What geometry does a learning system discover in data-and what does it impose through architecture, objectives, and representation How should we compare representations across models or modalities, and when do claims about manifolds, symmetry, topology, or emergent structure have mathematical content rather than visual appeal Geometric Foundations of AI (II) develops the object-side geometry of modern machine learning. It begins with group actions, representation theory, and equivariant architectures, then builds toward information bottlenecks, contrastive and self-supervised representation geometry, non-Euclidean and hyperbolic embeddings, manifold hypotheses and intrinsic dimension, representation diagnostics, alignment and transfer, topological data analysis, and the geometry of foundation-model representations. A recurring theme is that a geometric claim becomes meaningful only after the object, metric, symmetry, scale, and estimator have been specified. Written for advanced undergraduates, graduate students, and researchers in machine learning, statistics, geometry, and applied mathematics, the book combines exact mathematics with carefully delimited model-dependent and empirical claims. It is both a technical foundation for geometric and representation learning and a guide to deciding which geometric interpretations are justified by the evidence.…

Taschenbuch. Condition: Neu. Neuware - Continuous-time mathematics has become a common language across modern artificial intelligence. Yet the same words-gradient flow, diffusion, entropy, sampling, control, and equilibrium-often refer to different mathematical objects, metrics, and regimes. This book is built around making those distinctions precise. Stochastic Dynamics for AI develops deterministic and stochastic dynamics from first principles and then uses them to connect Fokker-Planck equations and Wasserstein gradient flows; diffusion and flow-based generative models; Langevin, Hamiltonian, and non-reversible sampling; particle and variational inference; stochastic-gradient dynamics and the edge of stability; entropy-regularized control and continuous-time reinforcement learning; mean-field limits and games; and path-space relative entropy and Schrödinger bridges. Rather than presenting these subjects as isolated toolkits, the book asks a harder question: when does an idea transfer from one domain to another Each major connection is separated into what is exact, what requires a limiting argument or a change of representation, and what fails outside its stated assumptions. Scope boxes, counterexamples, comparison tables, and four tiers of exercises make the hypotheses-and the boundaries of the conclusions-part of the mathematics. Designed for advanced undergraduates, graduate students, and researchers in mathematics, statistics, machine learning, and related fields, this volume offers a rigorous guide to the continuous-time structures that underlie learning, generation, sampling, optimization, control, and interacting AI systems.…

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

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