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Author :
Publisher :
ISBN 13 :
Total Pages : 840 pages
Book Rating : 4.3/5 (91 download)
Download or read book Proceedings of the ... Workshop on Neural Networks written by and published by . This book was released on 1991 with total page 840 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Zhaoquan Gu
Publisher : Springer Nature
ISBN 13 : 9819745195
Total Pages : 412 pages
Book Rating : 4.8/5 (197 download)
Download or read book Network Simulation and Evaluation written by Zhaoquan Gu and published by Springer Nature. This book was released on with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Martin T. Hagan
Publisher :
ISBN 13 : 9789812403766
Total Pages : pages
Book Rating : 4.4/5 (37 download)
Download or read book Neural Network Design written by Martin T. Hagan and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Alberto Griggio
Publisher : TU Wien Academic Press
ISBN 13 : 3854480539
Total Pages : 405 pages
Book Rating : 4.8/5 (544 download)
Download or read book PROCEEDINGS OF THE 22ND CONFERENCE ON FORMAL METHODS IN COMPUTER-AIDED DESIGN – FMCAD 2022 written by Alberto Griggio and published by TU Wien Academic Press. This book was released on 2022-10-12 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system testing.
Author : Society for Computer Simulation
Publisher :
ISBN 13 :
Total Pages : 836 pages
Book Rating : 4.:/5 (318 download)
Download or read book Proceedings of the Second Workshop on Neural Networks written by Society for Computer Simulation and published by . This book was released on 1991 with total page 836 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Alberto Marchisio
Publisher : CRC Press
ISBN 13 : 1040165036
Total Pages : 361 pages
Book Rating : 4.0/5 (41 download)
Download or read book Energy Efficiency and Robustness of Advanced Machine Learning Architectures written by Alberto Marchisio and published by CRC Press. This book was released on 2024-11-14 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient ML-based systems requires addressing three problems: energy-efficiency, robustness, and techniques that typically focus on optimizing for a single objective/have a limited set of goals. This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks. More specifically, this book improves the energy efficiency of complex models like CapsNets, through a specialized flow of hardware-level designs and software-level optimizations exploiting the application-driven knowledge of these systems and the error tolerance through approximations and quantization. This book also improves the robustness of ML models, in particular for SNNs executed on neuromorphic hardware, due to their inherent cost-effective features. This book integrates multiple optimization objectives into specialized frameworks for jointly optimizing the robustness and energy efficiency of these systems. This is an important resource for students and researchers of computer and electrical engineering who are interested in developing energy efficient and robust ML.
Author : Aneesh Sreevallabh Chivukula
Publisher : Springer Nature
ISBN 13 : 3030997723
Total Pages : 316 pages
Book Rating : 4.0/5 (39 download)
Download or read book Adversarial Machine Learning written by Aneesh Sreevallabh Chivukula and published by Springer Nature. This book was released on 2023-03-06 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: A critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed. We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications. In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.
Author : Gang Wang
Publisher : Springer Nature
ISBN 13 : 3030878392
Total Pages : 163 pages
Book Rating : 4.0/5 (38 download)
Download or read book Deployable Machine Learning for Security Defense written by Gang Wang and published by Springer Nature. This book was released on 2021-09-24 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes selected and extended papers from the Second International Workshop on Deployable Machine Learning for Security Defense, MLHat 2021, held in August 2021. Due to the COVID-19 pandemic the conference was held online. The 6 full papers were thoroughly reviewed and selected from 7 qualified submissions. The papers are organized in topical sections on machine learning for security, and malware attack and defense.
Author : Stephen J. Andriole
Publisher : CRC Press
ISBN 13 : 1000943208
Total Pages : 288 pages
Book Rating : 4.0/5 (9 download)
Download or read book Cognitive Systems Engineering for User-computer Interface Design, Prototyping, and Evaluation written by Stephen J. Andriole and published by CRC Press. This book was released on 2023-05-31 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume seeks to answer the question: "Can findings from cognitive science enhance the user-computer interaction process?" In so doing, it recognizes that user-computer interfaces (UCIs) are often essential parts of an information or decision support system -- and often critical components of software-intensive systems of all kinds. From the outset, the authors note that the design, prototyping, and evaluation of user-computer interfaces are part of larger systems and are therefore ideally designed, developed, and evaluated as part of a larger design and developmental process or "life cycle." Thus, this book describes the process by which functional, nonfunctional, or display-oriented requirements are converted first into prototypes and then into working systems. While the process may at times seem almost mysterious, there is in fact a methodology that drives the process -- a methodology that is defined in terms of an adaptive life cycle. There are a number of steps or phases that comprise the standard life cycle, as well as methods, tools and techniques that permit each step to be taken. Describing the effort to implement this process to enhance user-computer interaction, this book presents a methodological approach that seeks to identify and apply findings from cognitive science to the design, prototyping, and evaluation of user-computer interfaces.
Author : Amita Kapoor
Publisher : Packt Publishing Ltd
ISBN 13 : 1803249773
Total Pages : 516 pages
Book Rating : 4.8/5 (32 download)
Download or read book Platform and Model Design for Responsible AI written by Amita Kapoor and published by Packt Publishing Ltd. This book was released on 2023-04-28 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Craft ethical AI projects with privacy, fairness, and risk assessment features for scalable and distributed systems while maintaining explainability and sustainability Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn risk assessment for machine learning frameworks in a global landscape Discover patterns for next-generation AI ecosystems for successful product design Make explainable predictions for privacy and fairness-enabled ML training Book Description AI algorithms are ubiquitous and used for tasks, from recruiting to deciding who will get a loan. With such widespread use of AI in the decision-making process, it's necessary to build an explainable, responsible, transparent, and trustworthy AI-enabled system. With Platform and Model Design for Responsible AI, you'll be able to make existing black box models transparent. You'll be able to identify and eliminate bias in your models, deal with uncertainty arising from both data and model limitations, and provide a responsible AI solution. You'll start by designing ethical models for traditional and deep learning ML models, as well as deploying them in a sustainable production setup. After that, you'll learn how to set up data pipelines, validate datasets, and set up component microservices in a secure and private way in any cloud-agnostic framework. You'll then build a fair and private ML model with proper constraints, tune the hyperparameters, and evaluate the model metrics. By the end of this book, you'll know the best practices to comply with data privacy and ethics laws, in addition to the techniques needed for data anonymization. You'll be able to develop models with explainability, store them in feature stores, and handle uncertainty in model predictions. What you will learn Understand the threats and risks involved in ML models Discover varying levels of risk mitigation strategies and risk tiering tools Apply traditional and deep learning optimization techniques efficiently Build auditable and interpretable ML models and feature stores Understand the concept of uncertainty and explore model explainability tools Develop models for different clouds including AWS, Azure, and GCP Explore ML orchestration tools such as Kubeflow and Vertex AI Incorporate privacy and fairness in ML models from design to deployment Who this book is for This book is for experienced machine learning professionals looking to understand the risks and leakages of ML models and frameworks, and learn to develop and use reusable components to reduce effort and cost in setting up and maintaining the AI ecosystem.
Author :
Publisher :
ISBN 13 :
Total Pages : 456 pages
Book Rating : 4.:/5 (31 download)
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1995 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.
Author : Sriram Sankaranarayanan
Publisher : Springer Nature
ISBN 13 : 3031308204
Total Pages : 615 pages
Book Rating : 4.0/5 (313 download)
Download or read book Tools and Algorithms for the Construction and Analysis of Systems written by Sriram Sankaranarayanan and published by Springer Nature. This book was released on 2023-04-19 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book constitutes the proceedings of the 29th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2023, which was held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2023, during April 22-27, 2023, in Paris, France. The 56 full papers and 6 short tool demonstration papers presented in this volume were carefully reviewed and selected from 169 submissions. The proceedings also contain 1 invited talk in full paper length, 13 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, flexibility, and efficiency of tools and algorithms for building computer-controlled systems.
Author : Frederica Darema
Publisher : Springer Nature
ISBN 13 : 3031279867
Total Pages : 937 pages
Book Rating : 4.0/5 (312 download)
Download or read book Handbook of Dynamic Data Driven Applications Systems written by Frederica Darema and published by Springer Nature. This book was released on 2023-10-16 with total page 937 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Second Volume in the series Handbook of Dynamic Data Driven Applications Systems (DDDAS) expands the scope of the methods and the application areas presented in the first Volume and aims to provide additional and extended content of the increasing set of science and engineering advances for new capabilities enabled through DDDAS. The methods and examples of breakthroughs presented in the book series capture the DDDAS paradigm and its scientific and technological impact and benefits. The DDDAS paradigm and the ensuing DDDAS-based frameworks for systems’ analysis and design have been shown to engender new and advanced capabilities for understanding, analysis, and management of engineered, natural, and societal systems (“applications systems”), and for the commensurate wide set of scientific and engineering fields and applications, as well as foundational areas. The DDDAS book series aims to be a reference source of many of the important research and development efforts conducted under the rubric of DDDAS, and to also inspire the broader communities of researchers and developers about the potential in their respective areas of interest, of the application and the exploitation of the DDDAS paradigm and the ensuing frameworks, through the examples and case studies presented, either within their own field or other fields of study. As in the first volume, the chapters in this book reflect research work conducted over the years starting in the 1990’s to the present. Here, the theory and application content are considered for: Foundational Methods Materials Systems Structural Systems Energy Systems Environmental Systems: Domain Assessment & Adverse Conditions/Wildfires Surveillance Systems Space Awareness Systems Healthcare Systems Decision Support Systems Cyber Security Systems Design of Computer Systems The readers of this book series will benefit from DDDAS theory advances such as object estimation, information fusion, and sensor management. The increased interest in Artificial Intelligence (AI), Machine Learning and Neural Networks (NN) provides opportunities for DDDAS-based methods to show the key role DDDAS plays in enabling AI capabilities; address challenges that ML-alone does not, and also show how ML in combination with DDDAS-based methods can deliver the advanced capabilities sought; likewise, infusion of DDDAS-like approaches in NN-methods strengthens such methods. Moreover, the “DDDAS-based Digital Twin” or “Dynamic Digital Twin”, goes beyond the traditional DT notion where the model and the physical system are viewed side-by-side in a static way, to a paradigm where the model dynamically interacts with the physical system through its instrumentation, (per the DDDAS feed-back control loop between model and instrumentation).
Author : Dr Sivaraju Kuraku
Publisher : JEC PUBLICATION
ISBN 13 : 9361751131
Total Pages : 186 pages
Book Rating : 4.3/5 (617 download)
Download or read book AI-DRIVEN CYBER DEFENSE: Enhancing Security with Machine Learning and Generative AI written by Dr Sivaraju Kuraku and published by JEC PUBLICATION. This book was released on with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: ......
Author : Xiaofeng Chen
Publisher : Springer Nature
ISBN 13 : 3030624609
Total Pages : 623 pages
Book Rating : 4.0/5 (36 download)
Download or read book Machine Learning for Cyber Security written by Xiaofeng Chen and published by Springer Nature. This book was released on 2020-11-10 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi’an, China in October 2020. The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning, Malware detection and analysis, Data mining, and Artificial Intelligence.
Author : Jörg Henkel
Publisher : Springer Nature
ISBN 13 : 303052017X
Total Pages : 606 pages
Book Rating : 4.0/5 (35 download)
Download or read book Dependable Embedded Systems written by Jörg Henkel and published by Springer Nature. This book was released on 2020-12-09 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems.
Author : Roozbeh Razavi-Far
Publisher : Springer Nature
ISBN 13 : 3031117484
Total Pages : 371 pages
Book Rating : 4.0/5 (311 download)
Download or read book Federated and Transfer Learning written by Roozbeh Razavi-Far and published by Springer Nature. This book was released on 2022-09-30 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.
