Deep neural networks (DNNs) are currently the dominant technology in Artificial Intelligence (AI) and have shown impressive performance in diverse applications, including autonomous driving, medical diagnosis, text generation, and logical reasoning. However, they lack transparency due to their black-box construction and are vulnerable to environmental and adversarial noise. These issues have caused concerns about their safety and trust when deployed in the real world. Although standard training optimizes the model's accuracy, it does not take into account desirable safety properties such as robustness, fairness, and monotonicity.
As a result, researchers have spent considerable time developing automated methods for building safe and trustworthy DNNs. Abstract interpretation has emerged as the most popular framework for efficiently analyzing realistic DNNs among the various approaches. However, due to fundamental differences in the computational structure of DNNs compared to traditional programs, developing efficient DNN analyzers has required tackling significantly different research challenges than those encountered for programs.
In this monograph, state-of-the-art approaches based on abstract interpretation for analyzing DNNs are described. These approaches include the design of new abstract domains, synthesis of novel abstract transformers, abstraction refinement, and incremental analysis. Discussed is how the analysis results can be used to: (i) formally check whether a trained DNN satisfies desired output and gradient-based safety properties, (ii) guide the model updates during training towards satisfying safety properties, and (iii) reliably explain and interpret the black-box workings of DNNs.
外文書商品之書封，為出版社提供之樣本。實際出貨商品，以出版社所提供之現有版本為主。部份書籍，因出版社供應狀況特殊，匯率將依實際狀況做調整。
無庫存之商品，在您完成訂單程序之後，將以空運的方式為你下單調貨。為了縮短等待的時間，建議您將外文書與其他商品分開下單，以獲得最快的取貨速度，平均調貨時間為1~2個月。
為了保護您的權益，「三民網路書店」提供會員七日商品鑑賞期(收到商品為起始日)。
若要辦理退貨，請在商品鑑賞期內寄回，且商品必須是全新狀態與完整包裝(商品、附件、發票、隨貨贈品等)否則恕不接受退貨。
