Zobrazeno 1 - 10
of 99
pro vyhledávání: '"Sun, Youcheng"'
Autor:
Chen, Jialuo, Wang, Jingyi, Zhang, Xiyue, Sun, Youcheng, Kwiatkowska, Marta, Chen, Jiming, Cheng, Peng
Due to the vast testing space, the increasing demand for effective and efficient testing of deep neural networks (DNNs) has led to the development of various DNN test case prioritization techniques. However, the fact that DNNs can deliver high-confid
Externí odkaz:
http://arxiv.org/abs/2409.09130
Training large language models (LLMs) requires a substantial investment of time and money. To get a good return on investment, the developers spend considerable effort ensuring that the model never produces harmful and offensive outputs. However, bad
Externí odkaz:
http://arxiv.org/abs/2407.11059
This paper introduces a tool for verifying Python programs, which, using type annotation and front-end processing, can harness the capabilities of a bounded model-checking (BMC) pipeline. It transforms an input program into an abstract syntax tree to
Externí odkaz:
http://arxiv.org/abs/2407.03472
Smart meters play a crucial role in enhancing energy management and efficiency, but they raise significant privacy concerns by potentially revealing detailed user behaviors through energy consumption patterns. Recent scholarly efforts have focused on
Externí odkaz:
http://arxiv.org/abs/2405.18888
Autor:
Zhang, Haonan, Wang, Dongxia, Sun, Zhu, Li, Yanhui, Sun, Youcheng, Liang, Huizhi, Wang, Wenhai
Recommender systems (RSs) are designed to provide personalized recommendations to users. Recently, knowledge graphs (KGs) have been widely introduced in RSs to improve recommendation accuracy. In this study, however, we demonstrate that RSs do not ne
Externí odkaz:
http://arxiv.org/abs/2404.03164
We present QNNRepair, the first method in the literature for repairing quantized neural networks (QNNs). QNNRepair aims to improve the accuracy of a neural network model after quantization. It accepts the full-precision and weight-quantized neural ne
Externí odkaz:
http://arxiv.org/abs/2306.13793
Autor:
Tihanyi, Norbert, Jain, Ridhi, Charalambous, Yiannis, Ferrag, Mohamed Amine, Sun, Youcheng, Cordeiro, Lucas C.
This paper introduces an innovative approach that combines Large Language Models (LLMs) with Formal Verification strategies for automatic software vulnerability repair. Initially, we employ Bounded Model Checking (BMC) to identify vulnerabilities and
Externí odkaz:
http://arxiv.org/abs/2305.14752
We present AIREPAIR, a platform for repairing neural networks. It features the integration of existing network repair tools. Based on AIREPAIR, one can run different repair methods on the same model, thus enabling the fair comparison of different rep
Externí odkaz:
http://arxiv.org/abs/2211.15387
We present a practical verification method for safety analysis of the autonomous driving system (ADS). The main idea is to build a surrogate model that quantitatively depicts the behaviour of an ADS in the specified traffic scenario. The safety prope
Externí odkaz:
http://arxiv.org/abs/2211.12733
Autor:
Usman, Muhammad, Sun, Youcheng, Gopinath, Divya, Dange, Rishi, Manolache, Luca, Pasareanu, Corina S.
Deep neural network (DNN) models, including those used in safety-critical domains, need to be thoroughly tested to ensure that they can reliably perform well in different scenarios. In this article, we provide an overview of structural coverage metri
Externí odkaz:
http://arxiv.org/abs/2208.03407