 |
ML Newsnew | past | best | rss | submit | about |
| Introduction to Neural Network Verification (arxiv.org) | |
| 3 points by matt_d on Sep 22, 2021 | hide | past | pdf | discuss on HN | |
|
In plain words: Formal verification proves mathematically that a neural network will behave safely for every input in a range, instead of just checking a sample of test cases. This book introduces the core ideas behind such proofs and shows how to adapt them to deep learning. AbstractDeep learning has transformed the way we think of software and what it can do. But deep neural networks are fragile and their behaviors are often surprising. In many settings, we need to provide formal guarantees on the safety, security, correctness, or robustness of neural networks. This book covers foundational ideas from formal verification and their adaptation to reasoning about neural networks and deep learning. | |
|
add comment on HN
Also discussed: Sep 2021 (65 points, 12 comments)
|