Graph Representation Learning

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

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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 and Deep Learning in Computational Toxicology

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Publisher : Springer Nature
ISBN 13 : 3031207300
Total Pages : 654 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Machine Learning and Deep Learning in Computational Toxicology by : Huixiao Hong

Download or read book Machine Learning and Deep Learning in Computational Toxicology written by Huixiao Hong and published by Springer Nature. This book was released on 2023-03-11 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of machine learning and deep learning algorithms, methods, architectures, and software tools that have been developed and widely applied in predictive toxicology. It compiles a set of recent applications using state-of-the-art machine learning and deep learning techniques in analysis of a variety of toxicological endpoint data. The contents illustrate those machine learning and deep learning algorithms, methods, and software tools and summarise the applications of machine learning and deep learning in predictive toxicology with informative text, figures, and tables that are contributed by the first tier of experts. One of the major features is the case studies of applications of machine learning and deep learning in toxicological research that serve as examples for readers to learn how to apply machine learning and deep learning techniques in predictive toxicology. This book is expected to provide a reference for practical applications of machine learning and deep learning in toxicological research. It is a useful guide for toxicologists, chemists, drug discovery and development researchers, regulatory scientists, government reviewers, and graduate students. The main benefit for the readers is understanding the widely used machine learning and deep learning techniques and gaining practical procedures for applying machine learning and deep learning in predictive toxicology.

Rough Sets

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Publisher : Springer Nature
ISBN 13 : 3031212444
Total Pages : 445 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Rough Sets by : JingTao Yao

Download or read book Rough Sets written by JingTao Yao and published by Springer Nature. This book was released on 2022-11-10 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the International Joint Conference on Rough Sets, IJCRS 2022, held in Suzhou, China, in November 2022. The 28 full papers included in this book were carefully reviewed and selected from 42 submissions. They were organized in topical sections as follows: Invited papers, IRSS President Forum; rough set theory and applications; granular computing and applications; classification and deep learning; conceptual knowledge discovery and machine learning based on three-way decisions and granular computing; uncertainty in three-way decisions; granular computing, and data science.

Machine Learning and Knowledge Discovery in Databases

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Publisher : Springer Nature
ISBN 13 : 3030676641
Total Pages : 783 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Frank Hutter

Download or read book Machine Learning and Knowledge Discovery in Databases written by Frank Hutter and published by Springer Nature. This book was released on 2021-02-24 with total page 783 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

Graph Neural Networks: Foundations, Frontiers, and Applications

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Publisher : Springer Nature
ISBN 13 : 9811660549
Total Pages : 701 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Graph Neural Networks: Foundations, Frontiers, and Applications by : Lingfei Wu

Download or read book Graph Neural Networks: Foundations, Frontiers, and Applications written by Lingfei Wu and published by Springer Nature. This book was released on 2022-01-03 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.

A Wavelet Tour of Signal Processing

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Publisher : Elsevier
ISBN 13 : 0080520839
Total Pages : 663 pages
Book Rating : 4.0/5 (85 download)

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Book Synopsis A Wavelet Tour of Signal Processing by : Stephane Mallat

Download or read book A Wavelet Tour of Signal Processing written by Stephane Mallat and published by Elsevier. This book was released on 1999-09-14 with total page 663 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended to serve as an invaluable reference for anyone concerned with the application of wavelets to signal processing. It has evolved from material used to teach "wavelet signal processing" courses in electrical engineering departments at Massachusetts Institute of Technology and Tel Aviv University, as well as applied mathematics departments at the Courant Institute of New York University and ÉcolePolytechnique in Paris. - Provides a broad perspective on the principles and applications of transient signal processing with wavelets - Emphasizes intuitive understanding, while providing the mathematical foundations and description of fast algorithms - Numerous examples of real applications to noise removal, deconvolution, audio and image compression, singularity and edge detection, multifractal analysis, and time-varying frequency measurements - Algorithms and numerical examples are implemented in Wavelab, which is a Matlab toolbox freely available over the Internet - Content is accessible on several level of complexity, depending on the individual reader's needs New to the Second Edition - Optical flow calculation and video compression algorithms - Image models with bounded variation functions - Bayes and Minimax theories for signal estimation - 200 pages rewritten and most illustrations redrawn - More problems and topics for a graduate course in wavelet signal processing, in engineering and applied mathematics

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

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Publisher : Springer Nature
ISBN 13 : 3030597199
Total Pages : 867 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 by : Anne L. Martel

Download or read book Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 written by Anne L. Martel and published by Springer Nature. This book was released on 2020-10-02 with total page 867 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography

Machine Learning and Knowledge Discovery in Databases

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Publisher : Springer Nature
ISBN 13 : 3031263901
Total Pages : 798 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Massih-Reza Amini

Download or read book Machine Learning and Knowledge Discovery in Databases written by Massih-Reza Amini and published by Springer Nature. This book was released on 2023-03-16 with total page 798 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chapters “On the Current State of Reproducibility and Reporting of Uncertainty for Aspect-Based SentimentAnalysis” and “Contextualized Graph Embeddings for Adverse Drug Event Detection” are licensed under theterms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). For further details see license information in the chapter.

Data-Driven Mathematical and Statistical Models of Online Social Networks

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Publisher : Frontiers Media SA
ISBN 13 : 2889745961
Total Pages : 194 pages
Book Rating : 4.8/5 (897 download)

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Book Synopsis Data-Driven Mathematical and Statistical Models of Online Social Networks by : Shudong Li

Download or read book Data-Driven Mathematical and Statistical Models of Online Social Networks written by Shudong Li and published by Frontiers Media SA. This book was released on 2022-03-07 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Graph Based Multimedia Analysis

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Publisher : Elsevier
ISBN 13 : 0443214867
Total Pages : 368 pages
Book Rating : 4.4/5 (432 download)

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Book Synopsis Graph Based Multimedia Analysis by : Ananda S Chowdhury

Download or read book Graph Based Multimedia Analysis written by Ananda S Chowdhury and published by Elsevier. This book was released on 2024-08-07 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph Based Multimedia Analysis applies concepts from graph theory to the problems of analyzing overabundant video data. Video data can be quite diverse: exocentric (captured by a standard camera) or egocentric (captured by a wearable device like Google Glass); of various durations (ranging from a few seconds to several hours); and could be from a single source or multiple sources. Efficient extraction of important information from such a large class of diverse video data can be overwhelming. The book, with its rich repertoire of theoretically elegant solutions, from graph theory in conjunction with deep learning, constrained optimization, and game theory, empowers the audience to achieve tasks like obtaining concise yet useful summaries and precisely recognizing single as well as multiple actions in a computationally efficient manner. The book provides a unique treatise on topics like egocentric video analysis and scalable video processing. - Addresses a number of challenging state-of-the-art problems in multimedia analysis like summarization, co-summarization, and action recognition - Handles a wide class of video with different genres, durations, and numbers - Applies a class of theoretically rich algorithms from the discipline of graph theory, in conjunction with deep learning, constrained optimization and game theory - Includes thorough complexity analyses of the proposed solutions, and an appendix containing implementable source codes

Advances in Knowledge Discovery and Data Mining

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Publisher : Springer
ISBN 13 : 3540332073
Total Pages : 902 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Advances in Knowledge Discovery and Data Mining by : Wee Keong Ng

Download or read book Advances in Knowledge Discovery and Data Mining written by Wee Keong Ng and published by Springer. This book was released on 2006-03-10 with total page 902 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2006, held in Singapore in April 2006. The 67 revised full papers and 33 revised short papers presented together with 3 invited talks were carefully reviewed and selected from 501 submissions. The papers are organized in topical sections on Classification, Ensemble Learning, Clustering, Support Vector Machines, Text and Document Mining, Web Mining, Bio-Data Mining, and more.

Machine Learning and Artificial Intelligence in Geosciences

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Publisher : Academic Press
ISBN 13 : 0128216840
Total Pages : 318 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Machine Learning and Artificial Intelligence in Geosciences by :

Download or read book Machine Learning and Artificial Intelligence in Geosciences written by and published by Academic Press. This book was released on 2020-09-22 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning techniques to describe fractured media, signal augmentation to improve the generalization of deep neural networks, deep generator priors for Bayesian seismic inversion, as well as a review on homogenization for seismology, and more. - Provides high-level reviews of the latest innovations in geophysics - Written by recognized experts in the field - Presents an essential publication for researchers in all fields of geophysics

Machine Intelligence, Tools, and Applications

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Publisher : Springer Nature
ISBN 13 : 3031653920
Total Pages : 435 pages
Book Rating : 4.0/5 (316 download)

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Book Synopsis Machine Intelligence, Tools, and Applications by : Satchidananda Dehuri

Download or read book Machine Intelligence, Tools, and Applications written by Satchidananda Dehuri and published by Springer Nature. This book was released on with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Machine Learning and Knowledge Discovery in Databases

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Publisher : Springer Nature
ISBN 13 : 3030461505
Total Pages : 799 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Ulf Brefeld

Download or read book Machine Learning and Knowledge Discovery in Databases written by Ulf Brefeld and published by Springer Nature. This book was released on 2020-05-01 with total page 799 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three volume proceedings LNAI 11906 – 11908 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, held in Würzburg, Germany, in September 2019. The total of 130 regular papers presented in these volumes was carefully reviewed and selected from 733 submissions; there are 10 papers in the demo track. The contributions were organized in topical sections named as follows: Part I: pattern mining; clustering, anomaly and outlier detection, and autoencoders; dimensionality reduction and feature selection; social networks and graphs; decision trees, interpretability, and causality; strings and streams; privacy and security; optimization. Part II: supervised learning; multi-label learning; large-scale learning; deep learning; probabilistic models; natural language processing. Part III: reinforcement learning and bandits; ranking; applied data science: computer vision and explanation; applied data science: healthcare; applied data science: e-commerce, finance, and advertising; applied data science: rich data; applied data science: applications; demo track. Chapter "Heavy-tailed Kernels Reveal a Finer Cluster Structure in t-SNE Visualisations" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Pattern Recognition and Computer Vision

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Publisher : Springer Nature
ISBN 13 : 9819985463
Total Pages : 520 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Pattern Recognition and Computer Vision by : Qingshan Liu

Download or read book Pattern Recognition and Computer Vision written by Qingshan Liu and published by Springer Nature. This book was released on 2024-01-26 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis.

Neural Information Processing

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Publisher : Springer Nature
ISBN 13 : 3031301056
Total Pages : 660 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Neural Information Processing by : Mohammad Tanveer

Download or read book Neural Information Processing written by Mohammad Tanveer and published by Springer Nature. This book was released on 2023-04-12 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 13623, 13624, and 13625 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022. The 146 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.

Advanced Intelligent Computing Technology and Applications

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Publisher : Springer Nature
ISBN 13 : 9819756669
Total Pages : 508 pages
Book Rating : 4.8/5 (197 download)

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Book Synopsis Advanced Intelligent Computing Technology and Applications by : De-Shuang Huang

Download or read book Advanced Intelligent Computing Technology and Applications written by De-Shuang Huang and published by Springer Nature. This book was released on with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: