Interpretable Representation Learning For Visual Intelligence

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Interpretable Artificial Intelligence: A Perspective of Granular Computing

Author : Witold Pedrycz
Publisher : Springer Nature
ISBN 13 : 3030649490
Total Pages : 430 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Interpretable Artificial Intelligence: A Perspective of Granular Computing by : Witold Pedrycz

Download or read book Interpretable Artificial Intelligence: A Perspective of Granular Computing written by Witold Pedrycz and published by Springer Nature. This book was released on 2021-03-26 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a comprehensive treatise on the recent pursuits of Artificial Intelligence (AI) – Explainable Artificial Intelligence (XAI) by casting the crucial features of interpretability and explainability in the original framework of Granular Computing. The innovative perspective established with the aid of information granules provides a high level of human centricity and transparency central to the development of AI constructs. The chapters reflect the breadth of the area and cover recent developments in the methodology, advanced algorithms and applications of XAI to visual analytics, knowledge representation, learning and interpretation. The book appeals to a broad audience including researchers and practitioners interested in gaining exposure to the rapidly growing body of knowledge in AI and intelligent systems.

Interpretable Machine Learning

Author : Christoph Molnar
Publisher : Lulu.com
ISBN 13 : 0244768528
Total Pages : 320 pages
Book Rating : 4.2/5 (447 download)

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Book Synopsis Interpretable Machine Learning by : Christoph Molnar

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

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)

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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.

Pattern Recognition

Author : Zeynep Akata
Publisher : Springer Nature
ISBN 13 : 3030712788
Total Pages : 504 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Pattern Recognition by : Zeynep Akata

Download or read book Pattern Recognition written by Zeynep Akata and published by Springer Nature. This book was released on 2021-03-16 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 42nd German Conference on Pattern Recognition, DAGM GCPR 2020, which took place during September 28 until October 1, 2020. The conference was planned to take place in Tübingen, Germany, but had to change to an online format due to the COVID-19 pandemic. The 34 papers presented in this volume were carefully reviewed and selected from a total of 89 submissions. They were organized in topical sections named: Normalizing Flow, Semantics, Physics, Camera Calibration and Computer Vision, Pattern Recognition, Machine Learning.

Conceptual Spaces: Elaborations and Applications

Author : Mauri Kaipainen
Publisher : Springer
ISBN 13 : 3030128008
Total Pages : 204 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Conceptual Spaces: Elaborations and Applications by : Mauri Kaipainen

Download or read book Conceptual Spaces: Elaborations and Applications written by Mauri Kaipainen and published by Springer. This book was released on 2019-06-25 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited book focuses on concepts and their applications using the theory of conceptual spaces, one of today’s most central tracks of cognitive science discourse. It features 15 papers based on topics presented at the Conceptual Spaces @ Work 2016 conference. The contributors interweave both theory and applications in their papers. Among the first mentioned are studies on metatheories, logical and systemic implications of the theory, as well as relations between concepts and language. Examples of the latter include explanatory models of paradigm shifts and evolution in science as well as dilemmas and issues of health, ethics, and education. The theory of conceptual spaces overcomes many translational issues between academic theoretization and practical applications. The paradigm is mainly associated with structural explanations, such as categorization and meronomy. However, the community has also been relating it to relations, functions, and systems. The book presents work that provides a geometric model for the representation of human conceptual knowledge that bridges the symbolic and the sub-conceptual levels of representation. The model has already proven to have a broad range of applicability beyond cognitive science and even across a number of disciplines related to concepts and representation.

Feature and Dimensionality Reduction for Clustering with Deep Learning

Author : Frederic Ros
Publisher : Springer Nature
ISBN 13 : 3031487435
Total Pages : 273 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis Feature and Dimensionality Reduction for Clustering with Deep Learning by : Frederic Ros

Download or read book Feature and Dimensionality Reduction for Clustering with Deep Learning written by Frederic Ros and published by Springer Nature. This book was released on 2024-01-22 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and "tricks" participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by “family” to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers.

Pattern Recognition and Machine Intelligence

Author : Pradipta Maji
Publisher : Springer Nature
ISBN 13 : 3031451708
Total Pages : 892 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis Pattern Recognition and Machine Intelligence by : Pradipta Maji

Download or read book Pattern Recognition and Machine Intelligence written by Pradipta Maji and published by Springer Nature. This book was released on 2023-12-16 with total page 892 pages. Available in PDF, EPUB and Kindle. Book excerpt: The LNCS volume constitutes the refereed proceedings of 10th International Conference, PReMI 2023, in Kolkata, India, in December 2023. The 91 full papers, presented together with abstracts of 6 keynote and invited talks, were carefully reviewed and selected from more than 300 submissions. The conference presents topics covering different aspects of pattern recognition and machine intelligence with real life state-of-the-art applications.

Computer Vision

Author : Richard Szeliski
Publisher : Springer Nature
ISBN 13 : 3030343723
Total Pages : 925 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Computer Vision by : Richard Szeliski

Download or read book Computer Vision written by Richard Szeliski and published by Springer Nature. This book was released on 2022-01-03 with total page 925 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos. More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference takes a scientific approach to the formulation of computer vision problems. These problems are then analyzed using the latest classical and deep learning models and solved using rigorous engineering principles. Topics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses Incorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented reality Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects Includes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decade Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, estimation theory, datasets, and software Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Author : Wojciech Samek
Publisher : Springer Nature
ISBN 13 : 3030289540
Total Pages : 435 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Explainable AI: Interpreting, Explaining and Visualizing Deep Learning by : Wojciech Samek

Download or read book Explainable AI: Interpreting, Explaining and Visualizing Deep Learning written by Wojciech Samek and published by Springer Nature. This book was released on 2019-09-10 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.

Pattern Recognition and Computer Vision

Author : Zhouchen Lin
Publisher : Springer Nature
ISBN 13 : 3030317234
Total Pages : 831 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Pattern Recognition and Computer Vision by : Zhouchen Lin

Download or read book Pattern Recognition and Computer Vision written by Zhouchen Lin and published by Springer Nature. This book was released on 2019-10-31 with total page 831 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 11857, 11858, and 11859 constitutes the refereed proceedings of the Second Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019, held in Xi’an, China, in November 2019. The 165 revised full papers presented were carefully reviewed and selected from 412 submissions. The papers have been organized in the following topical sections: Part I: Object Detection, Tracking and Recognition, Part II: Image/Video Processing and Analysis, Part III: Data Analysis and Optimization.

Pattern Recognition

Author : Christian Bauckhage
Publisher : Springer Nature
ISBN 13 : 3030926591
Total Pages : 734 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Pattern Recognition by : Christian Bauckhage

Download or read book Pattern Recognition written by Christian Bauckhage and published by Springer Nature. This book was released on 2022-01-13 with total page 734 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 43rd DAGM German Conference on Pattern Recognition, DAGM GCPR 2021, which was held during September 28 – October 1, 2021. The conference was planned to take place in Bonn, Germany, but changed to a virtual event due to the COVID-19 pandemic. The 46 papers presented in this volume were carefully reviewed and selected from 116 submissions. They were organized in topical sections as follows: machine learning and optimization; actions, events, and segmentation; generative models and multimodal data; labeling and self-supervised learning; applications; and 3D modelling and reconstruction.

Advances and Trends in Artificial Intelligence. Theory and Applications

Author : Hamido Fujita
Publisher : Springer Nature
ISBN 13 : 3031368193
Total Pages : 430 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Advances and Trends in Artificial Intelligence. Theory and Applications by : Hamido Fujita

Download or read book Advances and Trends in Artificial Intelligence. Theory and Applications written by Hamido Fujita and published by Springer Nature. This book was released on 2023-07-18 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: This double volume LNAI 13925-13926 constitutes the thoroughly refereed proceedings of the 36th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2023, held in Shanghai, China, in July 2023. The 50 full papers and 20 short papers presented were carefully reviewed and selected from 129 submissions. The IEA/AIE 2023 conference on applications of applied intelligent systems to solve real-life problems in all areas including business and finance, science, engineering, industry, cyberspace, bioinformatics, automation, robotics, medicine and biomedicine, and human-machine interactions.

Pattern Recognition and Computer Vision

Author : Qingshan Liu
Publisher : Springer Nature
ISBN 13 : 9819984351
Total Pages : 532 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 2023-12-23 with total page 532 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.

Artificial Intelligence and the Arts

Author : Penousal Machado
Publisher : Springer Nature
ISBN 13 : 3030594750
Total Pages : 381 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Artificial Intelligence and the Arts by : Penousal Machado

Download or read book Artificial Intelligence and the Arts written by Penousal Machado and published by Springer Nature. This book was released on 2021-10-13 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Emotions, creativity, aesthetics, artistic behavior, divergent thoughts, and curiosity are both fundamental to the human experience and instrumental in the development of human-centered artificial intelligence systems that can relate, communicate, and understand human motivations, desires, and needs. In this book the editors put forward two core propositions: creative artistic behavior is one of the key challenges of artificial intelligence research, and computer-assisted creativity and human-centered artificial intelligence systems are the driving forces for research in this area. The invited chapters examine computational creativity and more specifically systems that exhibit artistic behavior or can improve humans' creative and artistic abilities. The authors synthesize and reflect on current trends, identify core challenges and opportunities, and present novel contributions and applications in domains such as the visual arts, music, 3D environments, and games. The book will be valuable for researchers, creatives, and others engaged with the relationship between artificial intelligence and the arts.

AI Methods and Applications in 3D Technologies

Author : Roumen Kountchev (Deceased)
Publisher : Springer Nature
ISBN 13 : 981972144X
Total Pages : 398 pages
Book Rating : 4.8/5 (197 download)

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Book Synopsis AI Methods and Applications in 3D Technologies by : Roumen Kountchev (Deceased)

Download or read book AI Methods and Applications in 3D Technologies written by Roumen Kountchev (Deceased) and published by Springer Nature. This book was released on with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Smart Data Intelligence

Author : R. Asokan
Publisher : Springer Nature
ISBN 13 : 9819731917
Total Pages : 668 pages
Book Rating : 4.8/5 (197 download)

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Book Synopsis Smart Data Intelligence by : R. Asokan

Download or read book Smart Data Intelligence written by R. Asokan and published by Springer Nature. This book was released on with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Augmented Reality and Artificial Intelligence

Author : Vladimir Geroimenko
Publisher : Springer Nature
ISBN 13 : 3031271661
Total Pages : 383 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Augmented Reality and Artificial Intelligence by : Vladimir Geroimenko

Download or read book Augmented Reality and Artificial Intelligence written by Vladimir Geroimenko and published by Springer Nature. This book was released on 2023-04-29 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first research monograph that explores a new research field and practical applications produced by the combined use of two of the most advanced and powerful technologies available in today’s world – Artificial Intelligence (AI) and Augmented Reality (AR). It is written by a team of 50 researchers and practitioners from 16 countries, which has enabled a thorough coverage of emerging or previously unexplored subject areas. The authors consider practical, theoretical, and cultural aspects of “AI-powered AR” and “AR-enriched AI”, and their usage in a large variety of areas, such as education, medicine, healthcare, dentistry, pharmacy, active lifestyle, smart services, fashion, retail, recommender systems, and several others. Augmented Reality and Artificial Intelligence: The Fusion of Advanced Technologies is essential reading not only for researchers, practitioners and technology developers, but also for students (both graduates and undergraduates) and anyone who is interested in building a comprehensive understanding of the emerging fields of “intelligent augmented environments” and “artificial intelligence presented by augmented reality”.