Zobrazeno 1 - 10
of 13 213
pro vyhledávání: '"CHANG, CHENG"'
We investigate the conformational and dynamical properties of a partially active Rouse chain, where activity is localized within a specific segment, positioned at various locations along the chain and spanning any given length. Through analytical met
Externí odkaz:
http://arxiv.org/abs/2412.04175
Autor:
Gu, Yu, Zheng, Boyuan, Gou, Boyu, Zhang, Kai, Chang, Cheng, Srivastava, Sanjari, Xie, Yanan, Qi, Peng, Sun, Huan, Su, Yu
Language agents have demonstrated promising capabilities in automating web-based tasks, though their current reactive approaches still underperform largely compared to humans. While incorporating advanced planning algorithms, particularly tree search
Externí odkaz:
http://arxiv.org/abs/2411.06559
Autor:
Shi, Yichen, Tao, Zhuofu, Gao, Yuhao, Zhou, Tianjia, Chang, Cheng, Wang, Yaxing, Chen, Bingyu, Zhang, Genhao, Liu, Alvin, Yu, Zhiping, Lin, Ting-Jung, He, Lei
High-performance analog and mixed-signal (AMS) circuits are mainly full-custom designed, which is time-consuming and labor-intensive. A significant portion of the effort is experience-driven, which makes the automation of AMS circuit design a formida
Externí odkaz:
http://arxiv.org/abs/2411.13560
Autor:
Gou, Boyu, Wang, Ruohan, Zheng, Boyuan, Xie, Yanan, Chang, Cheng, Shu, Yiheng, Sun, Huan, Su, Yu
Multimodal large language models (MLLMs) are transforming the capabilities of graphical user interface (GUI) agents, facilitating their transition from controlled simulations to complex, real-world applications across various platforms. However, the
Externí odkaz:
http://arxiv.org/abs/2410.05243
Recent works leverage LLMs to roleplay realistic social scenarios, aiding novices in practicing their social skills. However, simulating sensitive interactions, such as in mental health, is challenging. Privacy concerns restrict data access, and coll
Externí odkaz:
http://arxiv.org/abs/2407.00870
Autor:
Yin, Zhangyue, Sun, Qiushi, Guo, Qipeng, Zeng, Zhiyuan, Li, Xiaonan, Sun, Tianxiang, Chang, Cheng, Cheng, Qinyuan, Wang, Ding, Mou, Xiaofeng, Qiu, Xipeng, Huang, XuanJing
Recent advancements in Chain-of-Thought prompting have facilitated significant breakthroughs for Large Language Models (LLMs) in complex reasoning tasks. Current research enhances the reasoning performance of LLMs by sampling multiple reasoning chain
Externí odkaz:
http://arxiv.org/abs/2405.12939
In this paper, we present a framework for convolutional coded Poisson receivers (CCPRs) that incorporates spatially coupled methods into the architecture of coded Poisson receivers (CPRs). We use density evolution equations to track the packet decodi
Externí odkaz:
http://arxiv.org/abs/2404.15756
Recent advances in Large Language Models (LLMs) have demonstrated the emergence of capabilities (learned skills) when the number of system parameters and the size of training data surpass certain thresholds. The exact mechanisms behind such phenomena
Externí odkaz:
http://arxiv.org/abs/2404.07009
Sampling critical testing scenarios is an essential step in intelligence testing for Automated Vehicles (AVs). However, due to the lack of prior knowledge on the distribution of critical scenarios in sampling space, we can hardly efficiently find the
Externí odkaz:
http://arxiv.org/abs/2405.00696
Autor:
Wu, Junda, Chang, Cheng-Chun, Yu, Tong, He, Zhankui, Wang, Jianing, Hou, Yupeng, McAuley, Julian
The long-tail recommendation is a challenging task for traditional recommender systems, due to data sparsity and data imbalance issues. The recent development of large language models (LLMs) has shown their abilities in complex reasoning, which can h
Externí odkaz:
http://arxiv.org/abs/2403.06447