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2023 Ieee Acm International Workshop On Interpretability And Robustness In Neural Software Engineering Intense
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Book Synopsis 2023 IEEE ACM International Workshop on Interpretability and Robustness in Neural Software Engineering (InteNSE) by : IEEE Staff
Download or read book 2023 IEEE ACM International Workshop on Interpretability and Robustness in Neural Software Engineering (InteNSE) written by IEEE Staff and published by . This book was released on 2023-05-14 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Code is the most recent modality of interest in the Machine Learning (ML) domain Learning based techniques have been shown to improve and revolutionize software engineering and analysis tasks, including code completion and synthesis, code captioning and documentation, code search and clone detection, recovering variable names and types, and software testing, verification, and debugging Recent studies have even shown the great potential of neural models of code for decomposing the natural language reasoning problem into programmatic steps and solving it by executing the generated programs
Book Synopsis 2023 IEEE ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest) by : IEEE Staff
Download or read book 2023 IEEE ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest) written by IEEE Staff and published by . This book was released on 2023-05-15 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: DeepTest is an interdisciplinary workshop targeting research at the intersection of software engineering and deep learning This workshop will explore issues related to Deep Learning applied to Software Engineering (DL4SE) Software Engineering applied to Deep Learning (SE4DL) Although the main focus is on Deep Learning, we also encourage submissions that are more broadly related to Machine Learning, as well as submissions related to (Deep) Reinforcement Learning
Book Synopsis 2023 IEEE ACM 45th International Conference on Software Engineering Software Engineering in Society (ICSE SEIS) by : IEEE Staff
Download or read book 2023 IEEE ACM 45th International Conference on Software Engineering Software Engineering in Society (ICSE SEIS) written by IEEE Staff and published by . This book was released on 2023-05-14 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We are interested in social, technical, and or socio technical research approaches that have been applied to investigate and explain societal problems in depth and or to address or to support solutions to societal problems We especially welcome papers studying diversity, inclusion, belonging, and representation Equally, we are interested in sharing case studies, success stories, failures and lessons learned from working in highly complex problem spaces such as climate change, public health, cyber security and democracy We are interested in software engineering tools, processes, architectures, methods, frameworks, and theories that are relevant in these settings SEIS authors are encouraged to contribute soundly motivated and novel research, both mature and emerging SEIS welcomes multi and inter disciplinary research showcasing how software engineering can contribute to the many dimensions of software embedded in and influencing society
Book Synopsis 2023 IEEE ACM 5th International Workshop on Software Engineering Research and Practices for the IoT (SERP4IoT) by : IEEE Staff
Download or read book 2023 IEEE ACM 5th International Workshop on Software Engineering Research and Practices for the IoT (SERP4IoT) written by IEEE Staff and published by . This book was released on 2023-05-20 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We face a new software crisis In 1968, computer scientists learned that developing robust software requires skills, methods, and tools Today, software and hardware engineers realize that developing a robust Internet of Things (IoT) also pushes the states of their art and practice Recent news illustrate the many problems faced by IoT from lack of inter operability to broken updates to massive security attacks In this context, the International Workshop on Software Engineering Research & Practices for Internet of Things (SERP4IoT) aims to provide a highly interactive forum for researchers and practitioners to address the challenges of, find solutions for, and share experiences with the development, release, and testing of robust software for IoT devices With the huge success of previous years, and the subsequent success of our special issue on the IEEE IoT Journal, we welcome researchers from all the world to participate in this workshop
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.
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.
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.
Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Book Synopsis Pattern Recognition and Machine Learning by : Christopher M. Bishop
Download or read book Pattern Recognition and Machine Learning written by Christopher M. Bishop and published by Springer. This book was released on 2016-08-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Download or read book Deep Learning on Graphs written by Yao Ma and published by Cambridge University Press. This book was released on 2021-09-23 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive text on foundations and techniques of graph neural networks with applications in NLP, data mining, vision and healthcare.
Book Synopsis Introduction to Algorithms, third edition by : Thomas H. Cormen
Download or read book Introduction to Algorithms, third edition written by Thomas H. Cormen and published by MIT Press. This book was released on 2009-07-31 with total page 1313 pages. Available in PDF, EPUB and Kindle. Book excerpt: The latest edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-based flow. Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor. The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, substantial additions to the chapter on recurrence (now called “Divide-and-Conquer”), and an appendix on matrices. It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks. Many exercises and problems have been added for this edition. The international paperback edition is no longer available; the hardcover is available worldwide.
Book Synopsis Neural Networks for Pattern Recognition by : Christopher M. Bishop
Download or read book Neural Networks for Pattern Recognition written by Christopher M. Bishop and published by Oxford University Press. This book was released on 1995-11-23 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical pattern recognition; Probability density estimation; Single-layer networks; The multi-layer perceptron; Radial basis functions; Error functions; Parameter optimization algorithms; Pre-processing and feature extraction; Learning and generalization; Bayesian techniques; Appendix; References; Index.
Book Synopsis Visual Attributes by : Rogerio Schmidt Feris
Download or read book Visual Attributes written by Rogerio Schmidt Feris and published by Springer. This book was released on 2017-03-21 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique text/reference provides a detailed overview of the latest advances in machine learning and computer vision related to visual attributes, highlighting how this emerging field intersects with other disciplines, such as computational linguistics and human-machine interaction. Topics and features: presents attribute-based methods for zero-shot classification, learning using privileged information, and methods for multi-task attribute learning; describes the concept of relative attributes, and examines the effectiveness of modeling relative attributes in image search applications; reviews state-of-the-art methods for estimation of human attributes, and describes their use in a range of different applications; discusses attempts to build a vocabulary of visual attributes; explores the connections between visual attributes and natural language; provides contributions from an international selection of world-renowned scientists, covering both theoretical aspects and practical applications.
Book Synopsis Malware Detection by : Mihai Christodorescu
Download or read book Malware Detection written by Mihai Christodorescu and published by Springer Science & Business Media. This book was released on 2007-03-06 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book captures the state of the art research in the area of malicious code detection, prevention and mitigation. It contains cutting-edge behavior-based techniques to analyze and detect obfuscated malware. The book analyzes current trends in malware activity online, including botnets and malicious code for profit, and it proposes effective models for detection and prevention of attacks using. Furthermore, the book introduces novel techniques for creating services that protect their own integrity and safety, plus the data they manage.
Book Synopsis Automated Technology for Verification and Analysis by : Shuvendu K. Lahiri
Download or read book Automated Technology for Verification and Analysis written by Shuvendu K. Lahiri and published by Springer. This book was released on 2018-09-29 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 16th International Symposium on Automated Technology for Verification and Analysis, ATVA 2018, held in Los Angeles, CA, USA in October 2018. The 27 full papers presented together with 5 short papers and 3 invited talks were carefully reviewed and selected from 82 submissions. The symposium is dedicated to the promotion of research on theoretical and practical aspects of automated analysis, verification and synthesis by providing a forum for interaction between the regional and the international research communities and industry in the field.
Book Synopsis Lifelong Machine Learning, Second Edition by : Zhiyuan Sun
Download or read book Lifelong Machine Learning, Second Edition written by Zhiyuan Sun and published by Springer Nature. This book was released on 2022-06-01 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.
Book Synopsis Visual Analysis of Humans by : Thomas B. Moeslund
Download or read book Visual Analysis of Humans written by Thomas B. Moeslund and published by Springer Science & Business Media. This book was released on 2011-10-08 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique text/reference provides a coherent and comprehensive overview of all aspects of video analysis of humans. Broad in coverage and accessible in style, the text presents original perspectives collected from preeminent researchers gathered from across the world. In addition to presenting state-of-the-art research, the book reviews the historical origins of the different existing methods, and predicts future trends and challenges. Features: with a Foreword by Professor Larry Davis; contains contributions from an international selection of leading authorities in the field; includes an extensive glossary; discusses the problems associated with detecting and tracking people through camera networks; examines topics related to determining the time-varying 3D pose of a person from video; investigates the representation and recognition of human and vehicular actions; reviews the most important applications of activity recognition, from biometrics and surveillance, to sports and driver assistance.
