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This collection presents papers from the 149th Annual Meeting & Exhibition of The Minerals, Metals & Materials Society.
This book presents the complete collection of peer-reviewed presentations at the 1999 Cognitive Science Society meeting, including papers, poster abstracts, and descriptions of conference symposia. For students and researchers in all areas of cognitive science.
This book presents selected, peer-reviewed proceedings of the 2nd International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020), held in the city of Nha Trang, Vietnam, from 12 to 15 November, 2020. The purpose of the conference is to explore and ensure an understanding of the critical aspects contributing to sustainable development, especially materials, machines and methods. The contributions published in this book come from authors representing universities, research institutes and industrial companies, and reflect the results of a very broad spectrum of research, from micro- and nanoscale materials design and processing, to mechanical engineering technology in industry. Many of the contributions selected for these proceedings focus on materials modeling, eco-material processes and mechanical manufacturing.
An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.
This collection presents papers from the 150th Annual Meeting & Exhibition of The Minerals, Metals & Materials Society.