Spatially Explicit Hyperparameter Optimization For Neural Networks

Download Spatially Explicit Hyperparameter Optimization For Neural Networks full books in PDF, epub, and Kindle. Read online Spatially Explicit Hyperparameter Optimization For Neural Networks ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Spatially Explicit Hyperparameter Optimization for Neural Networks

Author : Minrui Zheng
Publisher : Springer Nature
ISBN 13 : 9811653992
Total Pages : 120 pages
Book Rating : 4.8/5 (116 download)

DOWNLOAD NOW!

Book Synopsis Spatially Explicit Hyperparameter Optimization for Neural Networks by : Minrui Zheng

Download or read book Spatially Explicit Hyperparameter Optimization for Neural Networks written by Minrui Zheng and published by Springer Nature. This book was released on 2021-10-18 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks as the commonly used machine learning algorithms, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), have been extensively used in the GIScience domain to explore the nonlinear and complex geographic phenomena. However, there are a few studies that investigate the parameter settings of neural networks in GIScience. Moreover, the model performance of neural networks often depends on the parameter setting for a given dataset. Meanwhile, adjusting the parameter configuration of neural networks will increase the overall running time. Therefore, an automated approach is necessary for addressing these limitations in current studies. This book proposes an automated spatially explicit hyperparameter optimization approach to identify optimal or near-optimal parameter settings for neural networks in the GIScience field. Also, the approach improves the computing performance at both model and computing levels. This book is written for researchers of the GIScience field as well as social science subjects.

Spatially Explicit Hyperparameter Optimization for Neural Networks

Author : Minrui Zheng
Publisher :
ISBN 13 : 9789811654008
Total Pages : 0 pages
Book Rating : 4.6/5 (54 download)

DOWNLOAD NOW!

Book Synopsis Spatially Explicit Hyperparameter Optimization for Neural Networks by : Minrui Zheng

Download or read book Spatially Explicit Hyperparameter Optimization for Neural Networks written by Minrui Zheng and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks as the commonly used machine learning algorithms, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), have been extensively used in the GIScience domain to explore the nonlinear and complex geographic phenomena. However, there are a few studies that investigate the parameter settings of neural networks in GIScience. Moreover, the model performance of neural networks often depends on the parameter setting for a given dataset. Meanwhile, adjusting the parameter configuration of neural networks will increase the overall running time. Therefore, an automated approach is necessary for addressing these limitations in current studies. This book proposes an automated spatially explicit hyperparameter optimization approach to identify optimal or near-optimal parameter settings for neural networks in the GIScience field. Also, the approach improves the computing performance at both model and computing levels. This book is written for researchers of the GIScience field as well as social science subjects.

Landslide: Susceptibility, Risk Assessment and Sustainability

Author : Gopal Krishna Panda
Publisher : Springer Nature
ISBN 13 : 3031565916
Total Pages : 722 pages
Book Rating : 4.0/5 (315 download)

DOWNLOAD NOW!

Book Synopsis Landslide: Susceptibility, Risk Assessment and Sustainability by : Gopal Krishna Panda

Download or read book Landslide: Susceptibility, Risk Assessment and Sustainability written by Gopal Krishna Panda and published by Springer Nature. This book was released on with total page 722 pages. Available in PDF, EPUB and Kindle. Book excerpt:

AI 2023: Advances in Artificial Intelligence

Author : Tongliang Liu
Publisher : Springer Nature
ISBN 13 : 9819983886
Total Pages : 574 pages
Book Rating : 4.8/5 (199 download)

DOWNLOAD NOW!

Book Synopsis AI 2023: Advances in Artificial Intelligence by : Tongliang Liu

Download or read book AI 2023: Advances in Artificial Intelligence written by Tongliang Liu and published by Springer Nature. This book was released on 2023-11-26 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNAI 14471-14472 constitutes the refereed proceedings of the 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, held in Brisbane, QLD, Australia during November 28 – December 1, 2023. The 23 full papers presented together with 59 short papers were carefully reviewed and selected from 213 submissions. They are organized in the following topics: computer vision; deep learning; machine learning and data mining; optimization; medical AI; knowledge representation and NLP; explainable AI; reinforcement learning; and genetic algorithm.

ECAI 2020

Author : G. De Giacomo
Publisher : IOS Press
ISBN 13 : 164368101X
Total Pages : 3122 pages
Book Rating : 4.6/5 (436 download)

DOWNLOAD NOW!

Book Synopsis ECAI 2020 by : G. De Giacomo

Download or read book ECAI 2020 written by G. De Giacomo and published by IOS Press. This book was released on 2020-09-11 with total page 3122 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of more than 1,700 submissions was received for ECAI 2020, of which 1,443 were reviewed. Of these, 361 full-papers and 36 highlight papers were accepted (an acceptance rate of 25% for full-papers and 45% for highlight papers). The book is divided into three sections: ECAI full papers; ECAI highlight papers; and PAIS papers. The topics of these papers cover all aspects of AI, including Agent-based and Multi-agent Systems; Computational Intelligence; Constraints and Satisfiability; Games and Virtual Environments; Heuristic Search; Human Aspects in AI; Information Retrieval and Filtering; Knowledge Representation and Reasoning; Machine Learning; Multidisciplinary Topics and Applications; Natural Language Processing; Planning and Scheduling; Robotics; Safe, Explainable, and Trustworthy AI; Semantic Technologies; Uncertainty in AI; and Vision. The book will be of interest to all those whose work involves the use of AI technology.

Statistical Postprocessing of Ensemble Forecasts

Author : Stéphane Vannitsem
Publisher : Elsevier
ISBN 13 : 012812248X
Total Pages : 364 pages
Book Rating : 4.1/5 (281 download)

DOWNLOAD NOW!

Book Synopsis Statistical Postprocessing of Ensemble Forecasts by : Stéphane Vannitsem

Download or read book Statistical Postprocessing of Ensemble Forecasts written by Stéphane Vannitsem and published by Elsevier. This book was released on 2018-05-17 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Postprocessing of Ensemble Forecasts brings together chapters contributed by international subject-matter experts describing the current state of the art in the statistical postprocessing of ensemble forecasts. The book illustrates the use of these methods in several important applications including weather, hydrological and climate forecasts, and renewable energy forecasting. After an introductory section on ensemble forecasts and prediction systems, the second section of the book is devoted to exposition of the methods available for statistical postprocessing of ensemble forecasts: univariate and multivariate ensemble postprocessing are first reviewed by Wilks (Chapters 3), then Schefzik and Möller (Chapter 4), and the more specialized perspective necessary for postprocessing forecasts for extremes is presented by Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for evaluation of ensemble forecasts (Chapter 6 by Thorarinsdottir and Schuhen). The third section of this book is devoted to applications of ensemble postprocessing. Practical aspects of ensemble postprocessing are first detailed in Chapter 7 (Hamill), including an extended and illustrative case study. Chapters 8 (Hemri), 9 (Pinson and Messner), and 10 (Van Schaeybroeck and Vannitsem) discuss ensemble postprocessing specifically for hydrological applications, postprocessing in support of renewable energy applications, and postprocessing of long-range forecasts from months to decades. Finally, Chapter 11 (Messner) provides a guide to the ensemble-postprocessing software available in the R programming language, which should greatly help readers implement many of the ideas presented in this book. Edited by three experts with strong and complementary expertise in statistical postprocessing of ensemble forecasts, this book assesses the new and rapidly developing field of ensemble forecast postprocessing as an extension of the use of statistical corrections to traditional deterministic forecasts. Statistical Postprocessing of Ensemble Forecasts is an essential resource for researchers, operational practitioners, and students in weather, seasonal, and climate forecasting, as well as users of such forecasts in fields involving renewable energy, conventional energy, hydrology, environmental engineering, and agriculture. - Consolidates, for the first time, the methodologies and applications of ensemble forecasts in one succinct place - Provides real-world examples of methods used to formulate forecasts - Presents the tools needed to make the best use of multiple model forecasts in a timely and efficient manner

Multivariate Statistical Machine Learning Methods for Genomic Prediction

Author : Osval Antonio Montesinos López
Publisher : Springer Nature
ISBN 13 : 3030890104
Total Pages : 707 pages
Book Rating : 4.0/5 (38 download)

DOWNLOAD NOW!

Book Synopsis Multivariate Statistical Machine Learning Methods for Genomic Prediction by : Osval Antonio Montesinos López

Download or read book Multivariate Statistical Machine Learning Methods for Genomic Prediction written by Osval Antonio Montesinos López and published by Springer Nature. This book was released on 2022-02-14 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.

Coping with Floods

Author : Giuseppe Rossi
Publisher : Springer Science & Business Media
ISBN 13 : 9401110980
Total Pages : 756 pages
Book Rating : 4.4/5 (11 download)

DOWNLOAD NOW!

Book Synopsis Coping with Floods by : Giuseppe Rossi

Download or read book Coping with Floods written by Giuseppe Rossi and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt: Floods are natural hazards whose effects can deeply affect the economic and environmental equilibria of a region. Quality of life of people living in areas close to rivers depends on both the risk that a flood would occur and the reliability of flood forecast, warning and control systems. Tools for forecasting and mitigating floods have been developed through research in the recent past. Two innovations currently influence flood hazard mitigation, after many decades of lack of significant progress: they are the development of new technologies for real-time flood forecast and warning (based on weather radars and satellites) and a shift from structural to non-structural flood control measures, due to increased awareness of the importance of protecting the environment and the adverse impacts of hydraulic works on it. This book is a review of research progress booked in the improvements of forecast capability and the control of floods. Mostly the book presents the results of recent research in hydrology, modern techniques of real-time forecast and warning, and ways of controlling floods for smaller impacts on the environment. A number of case studies of floods in different geographical areas are also presented. Scientists and specialists working in fields of hydrology, environmental protection and hydraulic engineering will appreciate this book for its theoretical and practical content.

Flood Modeling, Prediction and Mitigation

Author : Zekâi Şen
Publisher : Springer
ISBN 13 : 3319523562
Total Pages : 431 pages
Book Rating : 4.3/5 (195 download)

DOWNLOAD NOW!

Book Synopsis Flood Modeling, Prediction and Mitigation by : Zekâi Şen

Download or read book Flood Modeling, Prediction and Mitigation written by Zekâi Şen and published by Springer. This book was released on 2017-11-03 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book draws on the author’s professional experience and expertise in humid and arid regions to familiarize readers with the basic scientific philosophy and methods regarding floods and their impacts on human life and property. The basis of each model, algorithm and calculation methodology is presented, together with logical and analytical strategies. Global warming and climate change trends are addressed, while flood risk assessments, vulnerability, preventive and mitigation procedures are explained systematically, helping readers apply them in a rational and effective manner. Lastly, real-world project applications are highlighted in each section, ensuring readers grasp not only the theoretical aspects but also their concrete implementation.

Graph Representation Learning

Author : William L. William L. Hamilton
Publisher : Springer Nature
ISBN 13 : 3031015886
Total Pages : 141 pages
Book Rating : 4.0/5 (31 download)

DOWNLOAD NOW!

Book Synopsis Graph Representation Learning by : William L. William L. Hamilton

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Machine Learning, Neural and Statistical Classification

Author : Donald Michie
Publisher : Prentice Hall
ISBN 13 :
Total Pages : 312 pages
Book Rating : 4.:/5 (318 download)

DOWNLOAD NOW!

Book Synopsis Machine Learning, Neural and Statistical Classification by : Donald Michie

Download or read book Machine Learning, Neural and Statistical Classification written by Donald Michie and published by Prentice Hall. This book was released on 1994 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Automated Machine Learning

Author : Frank Hutter
Publisher : Springer
ISBN 13 : 3030053180
Total Pages : 223 pages
Book Rating : 4.0/5 (3 download)

DOWNLOAD NOW!

Book Synopsis Automated Machine Learning by : Frank Hutter

Download or read book Automated Machine Learning written by Frank Hutter and published by Springer. This book was released on 2019-05-17 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

The Principles of Deep Learning Theory

Author : Daniel A. Roberts
Publisher : Cambridge University Press
ISBN 13 : 1316519333
Total Pages : 473 pages
Book Rating : 4.3/5 (165 download)

DOWNLOAD NOW!

Book Synopsis The Principles of Deep Learning Theory by : Daniel A. Roberts

Download or read book The Principles of Deep Learning Theory written by Daniel A. Roberts and published by Cambridge University Press. This book was released on 2022-05-26 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume develops an effective theory approach to understanding deep neural networks of practical relevance.

Efficient Processing of Deep Neural Networks

Author : Vivienne Sze
Publisher : Springer Nature
ISBN 13 : 3031017668
Total Pages : 254 pages
Book Rating : 4.0/5 (31 download)

DOWNLOAD NOW!

Book Synopsis Efficient Processing of Deep Neural Networks by : Vivienne Sze

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.

Machine Learning for Advanced Functional Materials

Author : Nirav Joshi
Publisher : Springer Nature
ISBN 13 : 9819903939
Total Pages : 306 pages
Book Rating : 4.8/5 (199 download)

DOWNLOAD NOW!

Book Synopsis Machine Learning for Advanced Functional Materials by : Nirav Joshi

Download or read book Machine Learning for Advanced Functional Materials written by Nirav Joshi and published by Springer Nature. This book was released on 2023-05-22 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the material’s electrical and mechanical properties based on available regression methods for supervised learning and optimization of material attributes. In summary, the growing interest among academics and professionals in the field of machine learning methods in functional nanomaterials such as sensors, solar cells, and photocatalysis is the driving force for behind this book. This is a comprehensive scientific reference book on machine learning for advanced functional materials and provides an in-depth examination of recent achievements in material science by focusing on topical issues using machine learning methods.

Clean Coastal Waters

Author : National Research Council
Publisher : National Academies Press
ISBN 13 : 0309069483
Total Pages : 422 pages
Book Rating : 4.3/5 (9 download)

DOWNLOAD NOW!

Book Synopsis Clean Coastal Waters by : National Research Council

Download or read book Clean Coastal Waters written by National Research Council and published by National Academies Press. This book was released on 2000-08-17 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: Environmental problems in coastal ecosystems can sometimes be attributed to excess nutrients flowing from upstream watersheds into estuarine settings. This nutrient over-enrichment can result in toxic algal blooms, shellfish poisoning, coral reef destruction, and other harmful outcomes. All U.S. coasts show signs of nutrient over-enrichment, and scientists predict worsening problems in the years ahead. Clean Coastal Waters explains technical aspects of nutrient over-enrichment and proposes both immediate local action by coastal managers and a longer-term national strategy incorporating policy design, classification of affected sites, law and regulation, coordination, and communication. Highlighting the Gulf of Mexico's "Dead Zone," the Pfiesteria outbreak in a tributary of Chesapeake Bay, and other cases, the book explains how nutrients work in the environment, why nitrogen is important, how enrichment turns into over-enrichment, and why some environments are especially susceptible. Economic as well as ecological impacts are examined. In addressing abatement strategies, the committee discusses the importance of monitoring sites, developing useful models of over-enrichment, and setting water quality goals. The book also reviews voluntary programs, mandatory controls, tax incentives, and other policy options for reducing the flow of nutrients from agricultural operations and other sources.

Cross-Modal Learning: Adaptivity, Prediction and Interaction

Author : Jianwei Zhang
Publisher : Frontiers Media SA
ISBN 13 : 2889762548
Total Pages : 295 pages
Book Rating : 4.8/5 (897 download)

DOWNLOAD NOW!

Book Synopsis Cross-Modal Learning: Adaptivity, Prediction and Interaction by : Jianwei Zhang

Download or read book Cross-Modal Learning: Adaptivity, Prediction and Interaction written by Jianwei Zhang and published by Frontiers Media SA. This book was released on 2023-02-02 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this Research Topic is to reflect and discuss links between neuroscience, psychology, computer science and robotics with regards to the topic of cross-modal learning which has, in recent years, emerged as a new area of interdisciplinary research. The term cross-modal learning refers to the synergistic synthesis of information from multiple sensory modalities such that the learning that occurs within any individual sensory modality can be enhanced with information from one or more other modalities. Cross-modal learning is a crucial component of adaptive behavior in a continuously changing world, and examples are ubiquitous, such as: learning to grasp and manipulate objects; learning to walk; learning to read and write; learning to understand language and its referents; etc. In all these examples, visual, auditory, somatosensory or other modalities have to be integrated, and learning must be cross-modal. In fact, the broad range of acquired human skills are cross-modal, and many of the most advanced human capabilities, such as those involved in social cognition, require learning from the richest combinations of cross-modal information. In contrast, even the very best systems in Artificial Intelligence (AI) and robotics have taken only tiny steps in this direction. Building a system that composes a global perspective from multiple distinct sources, types of data, and sensory modalities is a grand challenge of AI, yet it is specific enough that it can be studied quite rigorously and in such detail that the prospect for deep insights into these mechanisms is quite plausible in the near term. Cross-modal learning is a broad, interdisciplinary topic that has not yet coalesced into a single, unified field. Instead, there are many separate fields, each tackling the concerns of cross-modal learning from its own perspective, with currently little overlap. We anticipate an accelerating trend towards integration of these areas and we intend to contribute to that integration. By focusing on cross-modal learning, the proposed Research Topic can bring together recent progress in artificial intelligence, robotics, psychology and neuroscience.