Zobrazeno 1 - 10
of 33 454
pro vyhledávání: '"Liu, Ming"'
Autor:
Chu, Zheng, Chen, Jingchang, Chen, Qianglong, Wang, Haotian, Zhu, Kun, Du, Xiyuan, Yu, Weijiang, Liu, Ming, Qin, Bing
Large language models (LLMs) have demonstrated strong reasoning capabilities. Nevertheless, they still suffer from factual errors when tackling knowledge-intensive tasks. Retrieval-augmented reasoning represents a promising approach. However, signifi
Externí odkaz:
http://arxiv.org/abs/2406.19820
Autor:
Liang, Jiafeng, Jiang, Shixin, Wang, Zekun, Pan, Haojie, Chen, Zerui, Chu, Zheng, Liu, Ming, Fu, Ruiji, Wang, Zhongyuan, Qin, Bing
There are substantial instructional videos on the Internet, which provide us tutorials for completing various tasks. Existing instructional video datasets only focus on specific steps at the video level, lacking experiential guidelines at the task le
Externí odkaz:
http://arxiv.org/abs/2406.18227
Autor:
Chen, Hegan, Yang, Jichang, Chen, Jia, Wang, Songqi, Wang, Shaocong, Wang, Dingchen, Tian, Xinyu, Yu, Yifei, Chen, Xi, Lin, Yinan, He, Yangu, Wu, Xiaoshan, Li, Yi, Zhang, Xinyuan, Lin, Ning, Xu, Meng, Zhang, Xumeng, Wang, Zhongrui, Wang, Han, Shang, Dashan, Liu, Qi, Cheng, Kwang-Ting, Liu, Ming
Digital twins, the cornerstone of Industry 4.0, replicate real-world entities through computer models, revolutionising fields such as manufacturing management and industrial automation. Recent advances in machine learning provide data-driven methods
Externí odkaz:
http://arxiv.org/abs/2406.08343
Diffusion models have shown superior performance on unsupervised anomaly detection tasks. Since trained with normal data only, diffusion models tend to reconstruct normal counterparts of test images with certain noises added. However, these methods t
Externí odkaz:
http://arxiv.org/abs/2406.07487
In proactive dialogue, the challenge lies not just in generating responses but in steering conversations toward predetermined goals, a task where Large Language Models (LLMs) typically struggle due to their reactive nature. Traditional approaches to
Externí odkaz:
http://arxiv.org/abs/2406.05374
Planning is complicated by the combination of perception and map information, particularly when driving in heavy traffic. Developing an extendable and efficient representation that visualizes sensor noise and provides constraints to real-time plannin
Externí odkaz:
http://arxiv.org/abs/2406.04451
We theoretically study diverse exceptional points (EPs) in an experimentally feasible magno-optomechanics consisting of an optomechanical subsystem coupled to a magnomechanical subsystem via physically direct contact. By adiabatically eliminating bot
Externí odkaz:
http://arxiv.org/abs/2406.01060
Autor:
Harma, Simla Burcu, Chakraborty, Ayan, Kostenok, Elizaveta, Mishin, Danila, Ha, Dongho, Falsafi, Babak, Jaggi, Martin, Liu, Ming, Oh, Yunho, Subramanian, Suvinay, Yazdanbakhsh, Amir
The increasing size of deep neural networks necessitates effective model compression to improve computational efficiency and reduce their memory footprint. Sparsity and quantization are two prominent compression methods that have individually demonst
Externí odkaz:
http://arxiv.org/abs/2405.20935
Autor:
Chen, Jingchang, Tang, Hongxuan, Chu, Zheng, Chen, Qianglong, Wang, Zekun, Liu, Ming, Qin, Bing
Despite recent progress made by large language models in code generation, they still struggle with programs that meet complex requirements. Recent work utilizes plan-and-solve decomposition to decrease the complexity and leverage self-tests to refine
Externí odkaz:
http://arxiv.org/abs/2405.20092
Learning a skill generally relies on both practical experience by doer and insightful high-level guidance by instructor. Will this strategy also work well for solving complex non-convex optimization problems? Here, a common gradient-based optimizer a
Externí odkaz:
http://arxiv.org/abs/2405.19732