Zobrazeno 1 - 10
of 254
pro vyhledávání: '"Chen, TaoLue"'
Autor:
Yang, Guang, Zhou, Yu, Zhang, Xiangyu, Cheng, Wei, Liu, Ke, Chen, Xiang, Zhuo, Terry Yue, Chen, Taolue
The extensive application of Large Language Models (LLMs) in generative coding tasks has raised concerns due to their high computational demands and energy consumption. Unlike previous structural pruning methods designed for classification models tha
Externí odkaz:
http://arxiv.org/abs/2412.15921
Autonomous driving systems (ADS) have achieved remarkable progress in recent years. However, ensuring their safety and reliability remains a critical challenge due to the complexity and uncertainty of driving scenarios. In this paper, we focus on sim
Externí odkaz:
http://arxiv.org/abs/2412.13802
Reasoning about strategic abilities is key to AI systems comprising multiple agents, which provide a unified framework for formalizing various problems in game theory, social choice theory, etc. In this work, we propose a probabilistic extension of t
Externí odkaz:
http://arxiv.org/abs/2412.06509
The development of large language models (LLMs) has revolutionized automated code generation. However, their high demand of computation resources has hindered a broader deployment and raised environmental concerns. A common strategy for diminishing c
Externí odkaz:
http://arxiv.org/abs/2411.06680
Autor:
Yang, Guang, Zhou, Yu, Cheng, Wei, Zhang, Xiangyu, Chen, Xiang, Zhuo, Terry Yue, Liu, Ke, Zhou, Xin, Lo, David, Chen, Taolue
The widespread use of Large Language Models (LLMs) in software engineering has intensified the need for improved model and resource efficiency. In particular, for neural code generation, LLMs are used to translate function/method signature and DocStr
Externí odkaz:
http://arxiv.org/abs/2410.22793
Autor:
Li, Zenan, Huang, Yunpeng, Li, Zhaoyu, Yao, Yuan, Xu, Jingwei, Chen, Taolue, Ma, Xiaoxing, Lu, Jian
Neuro-symbolic systems combine the abilities of neural perception and logical reasoning. However, end-to-end learning of neuro-symbolic systems is still an unsolved challenge. This paper proposes a natural framework that fuses neural network training
Externí odkaz:
http://arxiv.org/abs/2410.20957
Autor:
Gao, Hao, Wang, Jingyue, Fang, Wenyang, Xu, Jingwei, Huang, Yunpeng, Chen, Taolue, Ma, Xiaoxing
Autonomous Driving Systems (ADS) require diverse and safety-critical traffic scenarios for effective training and testing, but the existing data generation methods struggle to provide flexibility and scalability. We propose LASER, a novel frame-work
Externí odkaz:
http://arxiv.org/abs/2410.16197
Code Language Models (CLMs), particularly those leveraging deep learning, have achieved significant success in code intelligence domain. However, the issue of security, particularly backdoor attacks, is often overlooked in this process. The previous
Externí odkaz:
http://arxiv.org/abs/2407.08956
Recent studies in neuro-symbolic learning have explored the integration of logical knowledge into deep learning via encoding logical constraints as an additional loss function. However, existing approaches tend to vacuously satisfy logical constraint
Externí odkaz:
http://arxiv.org/abs/2403.00329
Neuro-symbolic learning generally consists of two separated worlds, i.e., neural network training and symbolic constraint solving, whose success hinges on symbol grounding, a fundamental problem in AI. This paper presents a novel, softened symbol gro
Externí odkaz:
http://arxiv.org/abs/2403.00323