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Author: Aws Albarghouthi Publisher: ISBN: 9781680839104 Category : Languages : en Pages : 182
Book Description
Over the past decade, a number of hardware and software advances have conspired to thrust deep learning and neural networks to the forefront of computing. Deep learning has created a qualitative shift in our conception of what software is and what it can do: Every day we're seeing new applications of deep learning, from healthcare to art, and it feels like we're only scratching the surface of a universe of new possibilities. This book offers the first introduction of foundational ideas from automated verification as applied to deep neural networks and deep learning. It is divided into three parts: Part 1 defines neural networks as data-flow graphs of operators over real-valued inputs. Part 2 discusses constraint-based techniques for verification. Part 3 discusses abstraction-based techniques for verification. The book is a self-contained treatment of a topic that sits at the intersection of machine learning and formal verification. It can serve as an introduction to the field for first-year graduate students or senior undergraduates, even if they have not been exposed to deep learning or verification.
Author: Aws Albarghouthi Publisher: ISBN: 9781680839104 Category : Languages : en Pages : 182
Book Description
Over the past decade, a number of hardware and software advances have conspired to thrust deep learning and neural networks to the forefront of computing. Deep learning has created a qualitative shift in our conception of what software is and what it can do: Every day we're seeing new applications of deep learning, from healthcare to art, and it feels like we're only scratching the surface of a universe of new possibilities. This book offers the first introduction of foundational ideas from automated verification as applied to deep neural networks and deep learning. It is divided into three parts: Part 1 defines neural networks as data-flow graphs of operators over real-valued inputs. Part 2 discusses constraint-based techniques for verification. Part 3 discusses abstraction-based techniques for verification. The book is a self-contained treatment of a topic that sits at the intersection of machine learning and formal verification. It can serve as an introduction to the field for first-year graduate students or senior undergraduates, even if they have not been exposed to deep learning or verification.
Author: Brian J. Taylor Publisher: Springer Science & Business Media ISBN: 0387294856 Category : Computers Languages : en Pages : 280
Book Description
Neural networks are members of a class of software that have the potential to enable intelligent computational systems capable of simulating characteristics of biological thinking and learning. Currently no standards exist to verify and validate neural network-based systems. NASA Independent Verification and Validation Facility has contracted the Institute for Scientific Research, Inc. to perform research on this topic and develop a comprehensive guide to performing V&V on adaptive systems, with emphasis on neural networks used in safety-critical or mission-critical applications. Methods and Procedures for the Verification and Validation of Artificial Neural Networks is the culmination of the first steps in that research. This volume introduces some of the more promising methods and techniques used for the verification and validation (V&V) of neural networks and adaptive systems. A comprehensive guide to performing V&V on neural network systems, aligned with the IEEE Standard for Software Verification and Validation, will follow this book.
Author: Alexandra Silva Publisher: Springer Nature ISBN: 3030816850 Category : Computers Languages : en Pages : 922
Book Description
This open access two-volume set LNCS 12759 and 12760 constitutes the refereed proceedings of the 33rd International Conference on Computer Aided Verification, CAV 2021, held virtually in July 2021. The 63 full papers presented together with 16 tool papers and 5 invited papers were carefully reviewed and selected from 290 submissions. The papers were organized in the following topical sections: Part I: invited papers; AI verification; concurrency and blockchain; hybrid and cyber-physical systems; security; and synthesis. Part II: complexity and termination; decision procedures and solvers; hardware and model checking; logical foundations; and software verification. This is an open access book.
Author: Laura L. Pullum Publisher: John Wiley & Sons ISBN: 047008457X Category : Computers Languages : en Pages : 146
Book Description
This book provides guidance on the verification and validation of neural networks/adaptive systems. Considering every process, activity, and task in the lifecycle, it supplies methods and techniques that will help the developer or V&V practitioner be confident that they are supplying an adaptive/neural network system that will perform as intended. Additionally, it is structured to be used as a cross-reference to the IEEE 1012 standard.
Author: Shuvendu K. Lahiri Publisher: Springer Nature ISBN: 3030532887 Category : Computers Languages : en Pages : 682
Book Description
The open access two-volume set LNCS 12224 and 12225 constitutes the refereed proceedings of the 32st International Conference on Computer Aided Verification, CAV 2020, held in Los Angeles, CA, USA, in July 2020.* The 43 full papers presented together with 18 tool papers and 4 case studies, were carefully reviewed and selected from 240 submissions. The papers were organized in the following topical sections: Part I: AI verification; blockchain and Security; Concurrency; hardware verification and decision procedures; and hybrid and dynamic systems. Part II: model checking; software verification; stochastic systems; and synthesis. *The conference was held virtually due to the COVID-19 pandemic.
Author: Changliu Liu Publisher: ISBN: 9781680837865 Category : Languages : en Pages :
Book Description
Neural networks have been widely used in many applications, such as image classification and understanding, language processing, and control of autonomous systems. These networks work by mapping inputs to outputs through a sequence of layers. At each layer, the input to that layer undergoes an affine transformation followed by a simple nonlinear transformation before being passed to the next layer. Neural networks are being used for increasingly important tasks, and in some cases, incorrect outputs can lead to costly consequences, hence validation of correctness at each layer is vital. The sheer size of the networks makes this not feasible using traditional methods. In this monograph, the authors survey a class of methods that are capable of formally verifying properties of deep neural networks. In doing so, they introduce a unified mathematical framework for verifying neural networks, classify existing methods under this framework, provide pedagogical implementations of existing methods, and compare those methods on a set of benchmark problems. Algorithms for Verifying Deep Neural Networks serves as a tutorial for students and professionals interested in this emerging field as well as a benchmark to facilitate the design of new verification algorithms.
Author: Kevin Gurney Publisher: CRC Press ISBN: 1482286998 Category : Computers Languages : en Pages : 234
Book Description
Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.
Author: Sivanandam S., Paulraj M Publisher: Vikas Publishing House ISBN: 9788125914259 Category : Computers Languages : en Pages : 240
Book Description
This fundamental book on Artificial Neural Networks has its emphasis on clear concepts, ease of understanding and simple examples. Written for undergraduate students, the book presents a large variety of standard neural networks with architecture, algorithms and applications.
Author: Kristin Yvonne Rozier Publisher: Springer Nature ISBN: 3031331702 Category : Computers Languages : en Pages : 508
Book Description
This book constitutes the proceedings of the 15th International Symposium on NASA Formal Methods, NFM 2023, held in Houston, Texas, USA, during May 16-18, 2023. The 26 full and 3 short papers presented in this volume were carefully reviewed and selected from 75 submissions. The papers deal with advances in formal methods, formal methods techniques, and formal methods in practice.
Author: Isil Dillig Publisher: Springer ISBN: 3030255409 Category : Computers Languages : en Pages : 680
Book Description
This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency.
Author: Aws Albarghouthi
Author: Aws Albarghouthi
Author: Brian J. Taylor
Author: Alexandra Silva
Author: Laura L. Pullum
Author: Shuvendu K. Lahiri
Author: Changliu Liu
Author: Kevin Gurney
Author: Sivanandam S., Paulraj M
Author: Kristin Yvonne Rozier
Author: Isil Dillig