Download or read book Advanced Structured Prediction written by Sebastian Nowozin and published by MIT Press. This book was released on 2014-12-05 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of recent work in the field of structured prediction, the building of predictive machine learning models for interrelated and dependent outputs. The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning. Contributors Jonas Behr, Yutian Chen, Fernando De La Torre, Justin Domke, Peter V. Gehler, Andrew E. Gelfand, Sébastien Giguère, Amir Globerson, Fred A. Hamprecht, Minh Hoai, Tommi Jaakkola, Jeremy Jancsary, Joseph Keshet, Marius Kloft, Vladimir Kolmogorov, Christoph H. Lampert, François Laviolette, Xinghua Lou, Mario Marchand, André F. T. Martins, Ofer Meshi, Sebastian Nowozin, George Papandreou, Daniel Průša, Gunnar Rätsch, Amélie Rolland, Bogdan Savchynskyy, Stefan Schmidt, Thomas Schoenemann, Gabriele Schweikert, Ben Taskar, Sinisa Todorovic, Max Welling, David Weiss, Thomáš Werner, Alan Yuille, Stanislav Živný

Download or read book Advanced Structured Prediction written by Sebastian Nowozin and published by MIT Press. This book was released on 2014-11-21 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of recent work in the field of structured prediction, the building of predictive machine learning models for interrelated and dependent outputs. The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning. Contributors Jonas Behr, Yutian Chen, Fernando De La Torre, Justin Domke, Peter V. Gehler, Andrew E. Gelfand, Sébastien Giguère, Amir Globerson, Fred A. Hamprecht, Minh Hoai, Tommi Jaakkola, Jeremy Jancsary, Joseph Keshet, Marius Kloft, Vladimir Kolmogorov, Christoph H. Lampert, François Laviolette, Xinghua Lou, Mario Marchand, André F. T. Martins, Ofer Meshi, Sebastian Nowozin, George Papandreou, Daniel Průša, Gunnar Rätsch, Amélie Rolland, Bogdan Savchynskyy, Stefan Schmidt, Thomas Schoenemann, Gabriele Schweikert, Ben Taskar, Sinisa Todorovic, Max Welling, David Weiss, Thomáš Werner, Alan Yuille, Stanislav Živný

Download or read book Modern Methods of Crystal Structure Prediction written by Artem R. Oganov and published by John Wiley & Sons. This book was released on 2011-08-04 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gathering leading specialists in the field of structure prediction, this book provides a unique view of this complex and rapidly developing field, reflecting the numerous viewpoints of the different authors. A summary of the major achievements over the last few years and of the challenges still remaining makes this monograph very timely.

Download or read book RNA Structure Prediction written by Risa Karakida Kawaguchi and published by Springer Nature. This book was released on 2023-01-27 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores recent progress in RNA secondary, tertiary structure prediction, and its application from an expansive point of view. Because of advancements in experimental protocols and devices, the integration of new types of data as well as new analysis techniques is necessary, and this volume discusses additional topics that are closely related to RNA structure prediction, such as the detection of structure-disrupting mutations, high-throughput structure analysis, and 3D structure design. Written for the highly successful Methods in Molecular Biology series, chapters feature the kind of detailed implementation advice that leads to quality research results. Authoritative and practical, RNA Structure Prediction serves as a valuable guide for both experimental and computational RNA researchers.

Download or read book Structured Learning and Prediction in Computer Vision written by Sebastian Nowozin and published by Now Publishers Inc. This book was released on 2011 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: Structured Learning and Prediction in Computer Vision introduces the reader to the most popular classes of structured models in computer vision.

Download or read book Special Issue on Structured Prediction written by Charles Parker and published by . This book was released on 2009 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Learning Structured Prediction Models written by Ben Taskar and published by . This book was released on 2004 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Crystal structure prediction using neural network potential and age fitness Pareto genetic algorithm written by Sadman Sadeed Omee and published by OAE Publishing Inc.. This book was released on 2024-03-02 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: While crystal structure prediction (CSP) remains a longstanding challenge, we introduce ParetoCSP, a novel algorithm for CSP, which combines a multi-objective genetic algorithm (GA) with a neural network inter-atomic potential model to find energetically optimal crystal structures given chemical compositions. We enhance the updated multi-objective GA (NSGA-III) by incorporating the genotypic age as an independent optimization criterion and employ the M3GNet universal inter-atomic potential to guide the GA search. Compared to GN-OA, a state-of-the-art neural potential-based CSP algorithm, ParetoCSP demonstrated significantly better predictive capabilities, outperforming by a factor of 2.562 across 55 diverse benchmark structures, as evaluated by seven performance metrics. Trajectory analysis of the traversed structures of all algorithms shows that ParetoCSP generated more valid structures than other algorithms, which helped guide the GA to search more effectively for the optimal structures. Our implementation code is available at https://github.com/sadmanomee/ParetoCSP.

Download or read book Introduction to Protein Structure Prediction written by Huzefa Rangwala and published by John Wiley & Sons. This book was released on 2011-03-16 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: A look at the methods and algorithms used to predict protein structure A thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structures. This indispensable book covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, readers will find an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction and they will acquire unique insight into the future applications of the modeled protein structures. The book begins with a thorough introduction to the protein structure prediction problem and is divided into four themes: a background on structure prediction, the prediction of structural elements, tertiary structure prediction, and functional insights. Within those four sections, the following topics are covered: Databases and resources that are commonly used for protein structure prediction The structure prediction flagship assessment (CASP) and the protein structure initiative (PSI) Definitions of recurring substructures and the computational approaches used for solving sequence problems Difficulties with contact map prediction and how sophisticated machine learning methods can solve those problems Structure prediction methods that rely on homology modeling, threading, and fragment assembly Hybrid methods that achieve high-resolution protein structures Parts of the protein structure that may be conserved and used to interact with other biomolecules How the loop prediction problem can be used for refinement of the modeled structures The computational model that detects the differences between protein structure and its modeled mutant Whether working in the field of bioinformatics or molecular biology research or taking courses in protein modeling, readers will find the content in this book invaluable.

Download or read book Protein Structure Prediction written by David Webster and published by Springer Science & Business Media. This book was released on 2008-02-03 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: The number of protein sequences grows each year, yet the number of structures deposited in the Protein Data Bank remains relatively small. The importance of protein structure prediction cannot be overemphasized, and this volume is a timely addition to the literature in this field. Protein Structure Prediction: Methods and Protocols is a departure from the normal Methods in Molecular Biology series format. By its very nature, protein structure prediction demands that there be a greater mix of theoretical and practical aspects than is normally seen in this series. This book is aimed at both the novice and the experienced researcher who wish for detailed inf- mation in the field of protein structure prediction; a major intention here is to include important information that is needed in the day-to-day work of a research scientist, important information that is not always decipherable in scientific literature. Protein Structure Prediction: Methods and Protocols covers the topic of protein structure prediction in an eclectic fashion, detailing aspects of pred- tion that range from sequence analysis (a starting point for many algorithms) to secondary and tertiary methods, on into the prediction of docked complexes (an essential point in order to fully understand biological function). As this volume progresses, the authors contribute their expert knowledge of protein structure prediction to many disciplines, such as the identification of motifs and domains, the comparative modeling of proteins, and ab initio approaches to protein loop, side chain, and protein prediction.

Download or read book Computational Methods for Protein Structure Prediction and Modeling written by Ying Xu and published by Springer Science & Business Media. This book was released on 2010-05-05 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volume Two of this two-volume sequence presents a comprehensive overview of protein structure prediction methods and includes protein threading, De novo methods, applications to membrane proteins and protein complexes, structure-based drug design, as well as structure prediction as a systems problem. A series of appendices review the biological and chemical basics related to protein structure, computer science for structural informatics, and prerequisite mathematics and statistics.

Download or read book Processing Analyzing and Learning of Images Shapes and Forms written by Xue-Cheng Tai and published by North Holland. This book was released on 2019-10 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt: Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2, Volume 20, surveys the contemporary developments relating to the analysis and learning of images, shapes and forms, covering mathematical models and quick computational techniques. Chapter cover Alternating Diffusion: A Geometric Approach for Sensor Fusion, Generating Structured TV-based Priors and Associated Primal-dual Methods, Graph-based Optimization Approaches for Machine Learning, Uncertainty Quantification and Networks, Extrinsic Shape Analysis from Boundary Representations, Efficient Numerical Methods for Gradient Flows and Phase-field Models, Recent Advances in Denoising of Manifold-Valued Images, Optimal Registration of Images, Surfaces and Shapes, and much more. Covers contemporary developments relating to the analysis and learning of images, shapes and forms Presents mathematical models and quick computational techniques relating to the topic Provides broad coverage, with sample chapters presenting content on Alternating Diffusion and Generating Structured TV-based Priors and Associated Primal-dual Methods

Download or read book Processing Analyzing and Learning of Images Shapes and Forms Part 2 written by and published by Elsevier. This book was released on 2019-10-16 with total page 706 pages. Available in PDF, EPUB and Kindle. Book excerpt: Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2, Volume 20, surveys the contemporary developments relating to the analysis and learning of images, shapes and forms, covering mathematical models and quick computational techniques. Chapter cover Alternating Diffusion: A Geometric Approach for Sensor Fusion, Generating Structured TV-based Priors and Associated Primal-dual Methods, Graph-based Optimization Approaches for Machine Learning, Uncertainty Quantification and Networks, Extrinsic Shape Analysis from Boundary Representations, Efficient Numerical Methods for Gradient Flows and Phase-field Models, Recent Advances in Denoising of Manifold-Valued Images, Optimal Registration of Images, Surfaces and Shapes, and much more. Covers contemporary developments relating to the analysis and learning of images, shapes and forms Presents mathematical models and quick computational techniques relating to the topic Provides broad coverage, with sample chapters presenting content on Alternating Diffusion and Generating Structured TV-based Priors and Associated Primal-dual Methods

Download or read book Enabling More Accurate and Efficient Structured Prediction written by David Weiss and published by . This book was released on 2013 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Perturbations Optimization and Statistics written by Tamir Hazan and published by MIT Press. This book was released on 2023-12-05 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees. In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules with improved theoretical guarantees. This book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art overview. Chapters address recent modeling ideas that have arisen within the perturbations framework, including Perturb & MAP, herding, and the use of neural networks to map generic noise to distribution over highly structured data. They describe new learning procedures for perturbation models, including an improved EM algorithm and a learning algorithm that aims to match moments of model samples to moments of data. They discuss understanding the relation of perturbation models to their traditional counterparts, with one chapter showing that the perturbations viewpoint can lead to new algorithms in the traditional setting. And they consider perturbation-based regularization in neural networks, offering a more complete understanding of dropout and studying perturbations in the context of deep neural networks.

Download or read book Structured prediction The Ultimate Step By Step Guide written by Gerardus Blokdyk and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Structured prediction The Ultimate Step-By-Step Guide.

Download or read book Prediction and Calculation of Crystal Structures written by Sule Atahan-Evrenk and published by Springer. This book was released on 2014-05-06 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: The series Topics in Current Chemistry presents critical reviews of the present and future trends in modern chemical research. The scope of coverage is all areas of chemical science including the interfaces with related disciplines such as biology, medicine and materials science. The goal of each thematic volume is to give the non-specialist reader, whether in academia or industry, a comprehensive insight into an area where new research is emerging which is of interest to a larger scientific audience. Each review within the volume critically surveys one aspect of that topic and places it within the context of the volume as a whole. The most significant developments of the last 5 to 10 years are presented using selected examples to illustrate the principles discussed. The coverage is not intended to be an exhaustive summary of the field or include large quantities of data, but should rather be conceptual, concentrating on the methodological thinking that will allow the non-specialist reader to understand the information presented. Contributions also offer an outlook on potential future developments in the field. Review articles for the individual volumes are invited by the volume editors. Readership: research chemists at universities or in industry, graduate students.