Zobrazeno 1 - 10
of 12 638
pro vyhledávání: '"Chang, Cheng"'
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
Intelligent vehicles and autonomous driving systems rely on scenario engineering for intelligence and index (I&I), calibration and certification (C&C), and verification and validation (V&V). To extract and index scenarios, various vehicle interaction
Externí odkaz:
http://arxiv.org/abs/2402.07720
Autor:
Chang, Cheng
Motion capture technology (MoCap) is a revolutionary method to translate real-world subjects’ movements into digital content across various industries, including robotics, medical devices, gaming, and biomechanics. This paper investigates how to ma
Externí odkaz:
https://hdl.handle.net/1721.1/152880
Autor:
Jin, Zhi, Xu, Sheng, Zhang, Xiang, Ling, Tianze, Dong, Nanqing, Ouyang, Wanli, Gao, Zhiqiang, Chang, Cheng, Sun, Siqi
De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research. Traditional de novo algorithms have encountered a bottleneck in accuracy due to the inherent complexity of proteomics data. While deep learning-bas
Externí odkaz:
http://arxiv.org/abs/2312.11584
Maxwell-Amp\`{e}re-Nernst-Planck (MANP) equations were recently proposed to model the dynamics of charged particles. In this study, we enhance a numerical algorithm of this system with deep learning tools. The proposed hybrid algorithm provides an au
Externí odkaz:
http://arxiv.org/abs/2312.05891