Introduction To Neural Network Verification
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Introduction To Neural Network Verification
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Author : Aws Albarghouthi
language : en
Publisher:
Release Date : 2021-12-02
Introduction To Neural Network Verification written by Aws Albarghouthi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-02 with categories.
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.
Introduction To Neural Network Verification A New Beginning 2 Neural Networks As Graphs 3 Correctness Properties 4 Logics And Satisfiability 5 Encodings Of Neural Networks 6 Dpll Modulo Theories 7 Neural Theory Solvers 8 Neural Interval Abstraction 9 Neural Zonotope Abstraction 10 Neural Polyhedron Abstraction 11 Verifying With Abstract Interpretation 12 Abstract Training Of Neural Networks 13 The Challenges Ahead Acknowledgements References
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Author : Aws Albarghouthi
language : en
Publisher:
Release Date : 2021
Introduction To Neural Network Verification A New Beginning 2 Neural Networks As Graphs 3 Correctness Properties 4 Logics And Satisfiability 5 Encodings Of Neural Networks 6 Dpll Modulo Theories 7 Neural Theory Solvers 8 Neural Interval Abstraction 9 Neural Zonotope Abstraction 10 Neural Polyhedron Abstraction 11 Verifying With Abstract Interpretation 12 Abstract Training Of Neural Networks 13 The Challenges Ahead Acknowledgements References written by Aws Albarghouthi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Electronic books categories.
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.
Methods And Procedures For The Verification And Validation Of Artificial Neural Networks
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Author : Brian J. Taylor
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-03-20
Methods And Procedures For The Verification And Validation Of Artificial Neural Networks written by Brian J. Taylor 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 2006-03-20 with Computers categories.
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.
Science Of Artificial Neural Networks
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Author :
language : en
Publisher:
Release Date : 1993
Science Of Artificial Neural Networks written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Neural networks (Computer science) categories.
Neural Network Verification For Nonlinear Systems
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Author : Chelsea Rose Sidrane
language : en
Publisher:
Release Date : 2022
Neural Network Verification For Nonlinear Systems written by Chelsea Rose Sidrane and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Machine learning has proven useful in a wide variety of domains from computer vision to control of autonomous systems. However, if we want to use neural networks in safety critical systems such as vehicles and aircraft, we need reliability guarantees. We turn to formal methods to verify that neural networks do not have unexpected behavior, such as misclassifying an image after a small amount of random noise is added. Within formal methods, there is a small but growing body of work focused on neural network verification. However, most of this work only reasons about neural networks in isolation, when in reality, neural networks are often used within large, complex systems. We build on this literature to verify neural networks operating within nonlinear systems. Our first contribution is to enable the use of mixed-integer linear programming for verification of systems containing both ReLU neural networks and smooth nonlinear functions. Mixed-integer linear programming is a common tool used for verifying neural networks with ReLU activation functions, and while effective, does not natively permit the use of nonlinear functions. We introduce an algorithm to overapproximate arbitrary nonlinear functions using piecewise linear constraints. These piecewise linear constraints can be encoded into a mixed-integer linear program, allowing verification of systems containing both ReLU neural networks and nonlinear functions. We use a special kind of approximation known as overapproximation which allows us to make sound claims about the original nonlinear system when we verify the overapproximate system. The next two contributions of this thesis are to apply the overapproximation algorithm to two different neural network verification settings: verifying inverse model neural networks and verifying neural network control policies. Frequently appearing in a variety of domains from medical imaging to state estimation, inverse problems involve reconstructing an underlying state from observations. The model mapping states to observations can be nonlinear and stochastic, making the inverse problem difficult. Neural networks are ideal candidates for solving inverse problems because they are very flexible and can be trained from data. However, inverse model neural networks lack built-in accuracy guarantees. We introduce a method to solve for verified upper bounds on the error of an inverse model neural network. The next verification setting we address is verifying neural network control policies for nonlinear dynamical systems. A control policy directs a dynamical system to perform a desired task such as moving to a target location. When a dynamical system is highly nonlinear and difficult to control, traditional control approaches may become computationally intractable. In contrast, neural network control policies are fast to execute. However, neural network control policies lack the stability, safety, and convergence guarantees that are often available to more traditional control approaches. In order to assess the safety and performance of neural network control policies, we introduce a method to perform finite time reachability analysis. Reachability analysis reasons about the set of states reachable by the dynamical system over time and whether that set of states is unsafe or is guaranteed to reach a goal. The final contribution of this thesis is the release of three open source software packages implementing methods described herein. The field of formal verification for neural networks is small and the release of open source software will allow it to grow more quickly as it makes iteration upon prior work easier. Overall, this thesis contributes ideas, methods, and tools to build confidence in deep learning systems. This area will continue to grow in importance as deep learning continues to find new applications.
Ijcnn International Joint Conference On Neural Networks
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Author :
language : en
Publisher:
Release Date : 1990
Ijcnn International Joint Conference On Neural Networks written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Artificial intelligence categories.
Neural Networks Applications
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Author : Patrick K. Simpson
language : en
Publisher: Institute of Electrical & Electronics Engineers(IEEE)
Release Date : 1996
Neural Networks Applications written by Patrick K. Simpson and has been published by Institute of Electrical & Electronics Engineers(IEEE) this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.
This volume builds on and continues the excellent coverage of the subject established in the first volume with a special focus on cutting-edge applications. This book provides practicing engineers with a snapshot of the latest applications, supported by the most recent developments in neural networks theory and technology. You'll find state-of-the-art coverage of applications in: control, power systems, medical systems, information processing, signal processing manufacturing, production and inspection, vehicular technology, and more!
The 1997 Ieee International Conference On Neural Networks June 9 12 1997 Westin Galleria Hotel Houston Texas Usa
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Author : IEEE Neural Networks Council
language : en
Publisher: Institute of Electrical & Electronics Engineers(IEEE)
Release Date : 1997
The 1997 Ieee International Conference On Neural Networks June 9 12 1997 Westin Galleria Hotel Houston Texas Usa written by IEEE Neural Networks Council and has been published by Institute of Electrical & Electronics Engineers(IEEE) this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Computers categories.
Instrumentation thrusts and achievements are reported in the field of simulation of aerospace dynamics. Quantified mapping techniques and measurements in research in unsteady fluid mechanics phenomena are described and the frontiers of speed and flight simulation are extended."
Neural Network Time Series
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Author : E. Michael Azoff
language : en
Publisher:
Release Date : 1994-09-27
Neural Network Time Series written by E. Michael Azoff and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-09-27 with Business & Economics categories.
Comprehensively specified benchmarks are provided (including weight values), drawn from time series examples in chaos theory and financial futures. The book covers data preprocessing, random walk theory, trading systems and risk analysis. It also provides a literature review, a tutorial on backpropagation, and a chapter on further reading and software.
Expert Systems
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Author : Cornelius T. Leondes
language : en
Publisher:
Release Date : 2002
Expert Systems written by Cornelius T. Leondes and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Expert systems (Computer science) categories.