Titlebook: Computer Aided Verification; 34th International C Sharon Shoham,Yakir Vizel Conference proceedings‘‘‘‘‘‘‘‘ 2022 The Editor(s) (if applicabl

法官所用

書(shū)目名稱(chēng)Computer Aided Verification影響因子(影響力)




書(shū)目名稱(chēng)Computer Aided Verification影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Computer Aided Verification網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Computer Aided Verification網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Computer Aided Verification被引頻次




書(shū)目名稱(chēng)Computer Aided Verification被引頻次學(xué)科排名




書(shū)目名稱(chēng)Computer Aided Verification年度引用




書(shū)目名稱(chēng)Computer Aided Verification年度引用學(xué)科排名




書(shū)目名稱(chēng)Computer Aided Verification讀者反饋




書(shū)目名稱(chēng)Computer Aided Verification讀者反饋學(xué)科排名

HERE

Program Verification with?Constrained Horn Clauses (Invited Paper)sed in a fragment of First-Order Logic called Constrained Horn Clauses (CHC). This transforms program analysis and verification tasks to the realm of first order satisfiability and into the realm of SMT solvers. In this paper, we give a brief overview of how CHCs capture verification problems for se

Congestion

Acclaim

CROW

Does a?Program Yield the?Right Distribution?wledge, . (provided the program terminates almost-surely). The class of distributions that can be specified in our formalism consists of standard distributions (geometric, uniform, etc.) and finite convolutions thereof. Our method relies on representing these (possibly infinite-support) distribution

不如屎殼郎

Abstraction-Refinement for?Hierarchical Probabilistic Modelsdels is subject to the famous state space explosion problem. We alleviate this problem by exploiting a hierarchical structure with repetitive parts. This structure not only occurs naturally in robotics, but also in probabilistic programs describing, e.g., network protocols. Such programs often repea

不如屎殼郎

Shared Certificates for?Neural Network Verificationn of reachable values at each layer. This process is repeated from scratch independently for each input (e.g., image) and perturbation (e.g., rotation), leading to an expensive overall proof effort when handling an entire dataset. In this work, we introduce a new method for reducing this verificatio

Pigeon

Example Guided Synthesis of?Linear Approximations for?Neural Network Verificationms involving neural networks. In the latter case, a linear approximation must be hand-crafted for the neural network’s activation functions. This hand-crafting is tedious, potentially error-prone, and requires an expert to prove the soundness of the linear approximation. Such a limitation is at odds

新陳代謝

Verifying Neural Networks Against Backdoor AttacksResearchers also discovered multiple security issues associated with neural networks. One of them is backdoor attacks, i.e., a neural network may be embedded with a backdoor such that a target output is almost always generated in the presence of a trigger. Existing defense approaches mostly focus on

浪費(fèi)物質(zhì)

: A CEGAR-Driven Training and?Verification Framework for?Safe Deep Reinforcement Learningustworthy when applied to safety-critical domains, which is typically achieved by formal verification performed after training. This . process has two limits: (i) trained systems are difficult to formally verify due to their continuous and infinite state space and inexplicable AI components (., deep