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pro vyhledávání: '"shi, Tianyu"'
Autor:
Qian, Kangan, Ma, Zhikun, He, Yangfan, Luo, Ziang, Shi, Tianyu, Zhu, Tianze, Li, Jiayin, Wang, Jianhui, Chen, Ziyu, He, Xiao, Shi, Yining, Fu, Zheng, Jiao, Xinyu, Jiang, Kun, Yang, Diange, Matsumaru, Takafumi
Ensuring safe, comfortable, and efficient navigation is a critical goal for autonomous driving systems. While end-to-end models trained on large-scale datasets excel in common driving scenarios, they often struggle with rare, long-tail events. Recent
Externí odkaz:
http://arxiv.org/abs/2411.18013
Autor:
Lei, Bin, Li, Yuchen, Zeng, Yiming, Ren, Tao, Luo, Yi, Shi, Tianyu, Gao, Zitian, Hu, Zeyu, Kang, Weitai, Chen, Qiuwu
Despite the impressive capabilities of large language models (LLMs), they currently exhibit two primary limitations, \textbf{\uppercase\expandafter{\romannumeral 1}}: They struggle to \textbf{autonomously solve the real world engineering problem}. \t
Externí odkaz:
http://arxiv.org/abs/2411.01114
Autor:
Li, Zeyuan, He, Yangfan, He, Lewei, Wang, Jianhui, Shi, Tianyu, Lei, Bin, Li, Yuchen, Chen, Qiuwu
Recently, large language models (LLMs) have achieved significant progress in automated code generation. Despite their strong instruction-following capabilities, these models frequently struggled to align with user intent in coding scenarios. In parti
Externí odkaz:
http://arxiv.org/abs/2410.21349
Navigating complex traffic environments has been significantly enhanced by advancements in intelligent technologies, enabling accurate environment perception and trajectory prediction for automated vehicles. However, existing research often neglects
Externí odkaz:
http://arxiv.org/abs/2410.16795
Large language models (LLMs) with long-context processing are still challenging because of their implementation complexity, training efficiency and data sparsity. To address this issue, a new paradigm named Online Long-context Processing (OLP) is pro
Externí odkaz:
http://arxiv.org/abs/2409.18014
Large language models (LLMs) have made significant advances in the field of natural language processing, but they still face challenges such as continuous decision-making. In this research, we propose a novel framework by integrating iterative feedba
Externí odkaz:
http://arxiv.org/abs/2409.00872
As large language models (LLMs) improve their capabilities in handling complex tasks, the issues of computational cost and efficiency due to long prompts are becoming increasingly prominent. To accelerate model inference and reduce costs, we propose
Externí odkaz:
http://arxiv.org/abs/2409.00855
Today's image generation systems are capable of producing realistic and high-quality images. However, user prompts often contain ambiguities, making it difficult for these systems to interpret users' potential intentions. Consequently, machines need
Externí odkaz:
http://arxiv.org/abs/2409.07464
Autor:
Yim, Yauwai, Chan, Chunkit, Shi, Tianyu, Deng, Zheye, Fan, Wei, Zheng, Tianshi, Song, Yangqiu
Large language models (LLMs) have shown success in handling simple games with imperfect information and enabling multi-agent coordination, but their ability to facilitate practical collaboration against other agents in complex, imperfect information
Externí odkaz:
http://arxiv.org/abs/2408.02559
Over the last decade, there has been increasing interest in autonomous driving systems. Reinforcement Learning (RL) shows great promise for training autonomous driving controllers, being able to directly optimize a combination of criteria such as eff
Externí odkaz:
http://arxiv.org/abs/2407.16857