Zobrazeno 1 - 10
of 16 946
pro vyhledávání: '"JIANG, BO"'
Autor:
Hu, Ruida, Peng, Chao, Ren, Jingyi, Jiang, Bo, Meng, Xiangxin, Wu, Qinyun, Gao, Pengfei, Wang, Xinchen, Gao, Cuiyun
Automatically resolving software issues is crucial for software development in practice, impacting the software quality and user experience. The process of resolving real-world issues encompasses tasks such as question-answering (QA), fault localizat
Externí odkaz:
http://arxiv.org/abs/2411.18019
Autor:
Liao, Bencheng, Chen, Shaoyu, Yin, Haoran, Jiang, Bo, Wang, Cheng, Yan, Sixu, Zhang, Xinbang, Li, Xiangyu, Zhang, Ying, Zhang, Qian, Wang, Xinggang
Recently, the diffusion model has emerged as a powerful generative technique for robotic policy learning, capable of modeling multi-mode action distributions. Leveraging its capability for end-to-end autonomous driving is a promising direction. Howev
Externí odkaz:
http://arxiv.org/abs/2411.15139
Autor:
Wu, Weiheng, Qiao, Wei, Yan, Wenhao, Jiang, Bo, Liu, Yuling, Liu, Baoxu, Lu, Zhigang, Liu, JunRong
Advanced Persistent Threats (APTs) are continuously evolving, leveraging their stealthiness and persistence to put increasing pressure on current provenance-based Intrusion Detection Systems (IDS). This evolution exposes several critical issues: (1)
Externí odkaz:
http://arxiv.org/abs/2411.02775
Aligning large language models (LLMs) with human intent is critical for enhancing their performance across a variety of tasks. Standard alignment techniques, such as Direct Preference Optimization (DPO), often rely on the binary Bradley-Terry (BT) mo
Externí odkaz:
http://arxiv.org/abs/2411.02442
Recently, graph prompt learning has garnered increasing attention in adapting pre-trained GNN models for downstream graph learning tasks. However, existing works generally conduct prompting over all graph elements (e.g., nodes, edges, node attributes
Externí odkaz:
http://arxiv.org/abs/2410.21749
Autor:
Jiang, Bo, Chen, Shaoyu, Liao, Bencheng, Zhang, Xingyu, Yin, Wei, Zhang, Qian, Huang, Chang, Liu, Wenyu, Wang, Xinggang
End-to-end autonomous driving demonstrates strong planning capabilities with large-scale data but still struggles in complex, rare scenarios due to limited commonsense. In contrast, Large Vision-Language Models (LVLMs) excel in scene understanding an
Externí odkaz:
http://arxiv.org/abs/2410.22313
In this paper, we consider a class of convex programming problems with linear equality constraints, which finds broad applications in machine learning and signal processing. We propose a new adaptive balanced augmented Lagrangian (ABAL) method for so
Externí odkaz:
http://arxiv.org/abs/2410.15358
In our previous works, we defined Local Information Privacy (LIP) as a context-aware privacy notion and presented the corresponding privacy-preserving mechanism. Then we claim that the mechanism satisfies epsilon-LIP for any epsilon>0 for arbitrary P
Externí odkaz:
http://arxiv.org/abs/2410.12309
Adapter-based tuning methods have shown significant potential in transferring knowledge from pre-trained Vision-Language Models to the downstream tasks. However, after reviewing existing adapters, we find they generally fail to fully explore the inte
Externí odkaz:
http://arxiv.org/abs/2410.07854
Although Large Language Models (LLMs) have achieved remarkable performance in numerous downstream tasks, their ubiquity has raised two significant concerns. One is that LLMs can hallucinate by generating content that contradicts relevant contextual i
Externí odkaz:
http://arxiv.org/abs/2410.03026