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
Download 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 PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get 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 book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
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
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
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.
Hacker's Delight
"This is the first book that promises to tell the deep, dark secrets of computer arithmetic, and it delivers in spades. It contains every trick I knew plus many, many more. A godsend for library developers, compiler writers, and lovers of elegant hacks, it deserves a spot on your shelf right next to Knuth." --Josh Bloch (Praise for the first edition) In Hacker’s Delight, Second Edition, Hank Warren once again compiles an irresistible collection of programming hacks: timesaving techniques, algorithms, and tricks that help programmers build more elegant and efficient software, while also gaining deeper insights into their craft. Warren’s hacks are eminently practical, but they’re also intrinsically interesting, and sometimes unexpected, much like the solution to a great puzzle. They are, in a word, a delight to any programmer who is excited by the opportunity to improve. Extensive additions in this edition include A new chapter on cyclic redundancy checking (CRC), including routines for the commonly used CRC-32 code A new chapter on error correcting codes (ECC), including routines for the Hamming code More coverage of integer division by constants, including methods using only shifts and adds Computing remainders without computing a quotient More coverage of population count and counting leading zeros Array population count New algorithms for compress and expand An LRU algorithm Floating-point to/from integer conversions Approximate floating-point reciprocal square root routine A gallery of graphs of discrete functions Now with exercises and answers