Introduction to Neural Network Verification - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Introduction to Neural Network Verification write by Aws Albarghouthi. This book was released on 2021-12-02. Introduction to Neural Network Verification available in PDF, EPUB and Kindle. 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
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 - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook 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 write by Aws Albarghouthi. This book was released on 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 available in PDF, EPUB and Kindle. 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
Methods and Procedures for the Verification and Validation of Artificial Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Methods and Procedures for the Verification and Validation of Artificial Neural Networks write by Brian J. Taylor. This book was released on 2006-03-20. Methods and Procedures for the Verification and Validation of Artificial Neural Networks available in PDF, EPUB and Kindle. 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.
Guidance for the Verification and Validation of Neural Networks
Guidance for the Verification and Validation of Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Guidance for the Verification and Validation of Neural Networks write by Laura L. Pullum. This book was released on 2007-03-09. Guidance for the Verification and Validation of Neural Networks available in PDF, EPUB and Kindle. 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.
Algorithms for Verifying Deep Neural Networks
Algorithms for Verifying Deep Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Algorithms for Verifying Deep Neural Networks write by Changliu Liu. This book was released on 2021-02-11. Algorithms for Verifying Deep Neural Networks available in PDF, EPUB and Kindle. 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.
