Deep Learning With Relational Logic Representations
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Deep Learning With Relational Logic Representations
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Author : G. Šír
language : en
Publisher: IOS Press
Release Date : 2022-11-23
Deep Learning With Relational Logic Representations written by G. Šír and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-23 with Computers categories.
Deep learning has been used with great success in a number of diverse applications, ranging from image processing to game playing, and the fast progress of this learning paradigm has even been seen as paving the way towards general artificial intelligence. However, the current deep learning models are still principally limited in many ways. This book, ‘Deep Learning with Relational Logic Representations’, addresses the limited expressiveness of the common tensor-based learning representation used in standard deep learning, by generalizing it to relational representations based in mathematical logic. This is the natural formalism for the relational data omnipresent in the interlinked structures of the Internet and relational databases, as well as for the background knowledge often present in the form of relational rules and constraints. These are impossible to properly exploit with standard neural networks, but the book introduces a new declarative deep relational learning framework called Lifted Relational Neural Networks, which generalizes the standard deep learning models into the relational setting by means of a ‘lifting’ paradigm, known from Statistical Relational Learning. The author explains how this approach allows for effective end-to-end deep learning with relational data and knowledge, introduces several enhancements and optimizations to the framework, and demonstrates its expressiveness with various novel deep relational learning concepts, including efficient generalizations of popular contemporary models, such as Graph Neural Networks. Demonstrating the framework across various learning scenarios and benchmarks, including computational efficiency, the book will be of interest to all those interested in the theory and practice of advancing representations of modern deep learning architectures.
Logical And Relational Learning
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Author : Luc De Raedt
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-09-27
Logical And Relational Learning written by Luc De Raedt and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-27 with Computers categories.
This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning.
Graph Representation Learning
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Author : William L. Hamilton
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Graph Representation Learning written by William L. Hamilton and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
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.
Compendium Of Neurosymbolic Artificial Intelligence
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Author : P. Hitzler
language : en
Publisher: IOS Press
Release Date : 2023-08-04
Compendium Of Neurosymbolic Artificial Intelligence written by P. Hitzler and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-04 with Computers categories.
If only it were possible to develop automated and trainable neural systems that could justify their behavior in a way that could be interpreted by humans like a symbolic system. The field of Neurosymbolic AI aims to combine two disparate approaches to AI; symbolic reasoning and neural or connectionist approaches such as Deep Learning. The quest to unite these two types of AI has led to the development of many innovative techniques which extend the boundaries of both disciplines. This book, Compendium of Neurosymbolic Artificial Intelligence, presents 30 invited papers which explore various approaches to defining and developing a successful system to combine these two methods. Each strategy has clear advantages and disadvantages, with the aim of most being to find some useful middle ground between the rigid transparency of symbolic systems and the more flexible yet highly opaque neural applications. The papers are organized by theme, with the first four being overviews or surveys of the field. These are followed by papers covering neurosymbolic reasoning; neurosymbolic architectures; various aspects of Deep Learning; and finally two chapters on natural language processing. All papers were reviewed internally before publication. The book is intended to follow and extend the work of the previous book, Neuro-symbolic artificial intelligence: The state of the art (IOS Press; 2021) which laid out the breadth of the field at that time. Neurosymbolic AI is a young field which is still being actively defined and explored, and this book will be of interest to those working in AI research and development.
Relational Data Mining
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Author : Saso Dzeroski
language : en
Publisher: Springer Science & Business Media
Release Date : 2001-08
Relational Data Mining written by Saso Dzeroski and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-08 with Business & Economics categories.
As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The first part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programming; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining. This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.
Logical And Relational Learning
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Author : Luc De Raedt
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-09-12
Logical And Relational Learning written by Luc De Raedt and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-12 with Computers categories.
This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning.
Representation Learning
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Author : Nada Lavrač
language : en
Publisher: Springer Nature
Release Date : 2021-07-10
Representation Learning written by Nada Lavrač and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-10 with Computers categories.
This monograph addresses advances in representation learning, a cutting-edge research area of machine learning. Representation learning refers to modern data transformation techniques that convert data of different modalities and complexity, including texts, graphs, and relations, into compact tabular representations, which effectively capture their semantic properties and relations. The monograph focuses on (i) propositionalization approaches, established in relational learning and inductive logic programming, and (ii) embedding approaches, which have gained popularity with recent advances in deep learning. The authors establish a unifying perspective on representation learning techniques developed in these various areas of modern data science, enabling the reader to understand the common underlying principles and to gain insight using selected examples and sample Python code. The monograph should be of interest to a wide audience, ranging from data scientists, machine learning researchers and students to developers, software engineers and industrial researchers interested in hands-on AI solutions.
Probabilistic Inductive Logic Programming
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Author : Luc De Raedt
language : en
Publisher: Springer
Release Date : 2008-02-26
Probabilistic Inductive Logic Programming written by Luc De Raedt and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-02-26 with Computers categories.
This book provides an introduction to probabilistic inductive logic programming. It places emphasis on the methods based on logic programming principles and covers formalisms and systems, implementations and applications, as well as theory.
Inductive Logic Programming
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Author : Rui Camacho
language : en
Publisher: Springer
Release Date : 2004-07-30
Inductive Logic Programming written by Rui Camacho and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-07-30 with Computers categories.
"How often we recall, with regret", wrote Mark Twain about editors, "that Napoleon once shot at a magazine editor and missed him and killed a publisher. But we remember with charity, that his intentions were good. " Fortunately, we live in more forgiving times, and are openly able to express our pleasure at being the editors of this volume containing the papers selected for presentation at the 14th International Conference on Inductive Logic Programming. ILP 2004 was held in Porto from the 6th to the 8th of September, under the auspices of the Department of Electrical Engineering and Computing of the Faculty of Engineering of the University of Porto (FEUP), and the Laborat ́ orio de Inteligˆ encia Arti?cial e Ciˆ encias da Computa ̧ c ̃ ao (LIACC). This annual me- ing of ILP practitioners and curious outsiders is intended to act as the premier forum for presenting the most recent and exciting work in the ?eld. Six invited talks--three from ?elds outside ILP, but nevertheless highly relevant to it-- and 20 full presentations formed the nucleus of the conference. It is the full-length papersofthese20presentationsthatcomprisethebulkofthisvolume. Asisnow common with the ILP conference, presentations made to a "Work-in-Progress" track will, hopefully, be available elsewhere. We gratefully acknowledge the continued support of Kluwer Academic P- lishers for the "Best Student Paper" award on behalf of the Machine Lea- ing journal; and Springer-Verlag for continuing to publish the proceedings of these conferences.
Logic Programming
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Author : Patricia M. Hill
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-07-24
Logic Programming written by Patricia M. Hill and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-24 with Computers categories.
This book constitutes the refereed proceedings of the 25th International Conference on Logic Programming, ICLP 2009, held in Pasadena, CA, USA, in July2009. The 29 revised full papers together with 9 short papers, 4 invited talks, 4 invited tutorials, and the abstracts of 18 doctoral consortium articles were carefully reviewed and selected from 69 initial submissions. The papers cover all issues of current research in logic programming, namely semantic foundations, formalisms, nonmonotonic reasoning, knowledge representation, compilation, memory management, virtual machines, parallelism, program analysis, program transformation, validation and verification, debugging, profiling, concurrency, objects, coordination, mobility, higher order, types, modes, programming techniques, abductive logic programming, answer set programming, constraint logic programming, inductive logic programming, alternative inference engines and mechanisms, deductive databases, data integration, software engineering, natural language, web tools, internet agents, artificial intelligence, bioinformatics.