Zobrazeno 1 - 10
of 14 460
pro vyhledávání: '"Xiao-Wen An"'
Autor:
Cheng, Yi, Liang, Xiao, Gong, Yeyun, Xiao, Wen, Wang, Song, Zhang, Yuji, Hou, Wenjun, Xu, Kaishuai, Liu, Wenge, Li, Wenjie, Jiao, Jian, Chen, Qi, Cheng, Peng, Xiong, Wayne
Self-consistency-based approaches, which involve repeatedly sampling multiple outputs and selecting the most consistent one as the final response, prove to be remarkably effective in improving the factual accuracy of large language models. Nonetheles
Externí odkaz:
http://arxiv.org/abs/2410.01556
Autor:
Shang, Xiao-Wen, Chen, Xuan, Hegade, Narendra N., Lan, Ze-Feng, Li, Xuan-Kun, Tang, Hao, Peng, Yu-Quan, Solano, Enrique, Jin, Xian-Min
Codesign, an integral part of computer architecture referring to the information interaction in hardware-software stack, is able to boost the algorithm mapping and execution in the computer hardware. This well applies to the noisy intermediate-scale
Externí odkaz:
http://arxiv.org/abs/2409.17930
In quantum mechanics, a long-standing question remains: How does a single photon traverse double slits? One intuitive picture suggests that the photon passes through only one slit, while its wavefunction splits into an ``empty" wave and a ``full" wav
Externí odkaz:
http://arxiv.org/abs/2409.13383
The ability of Graph Neural Networks (GNNs) to capture long-range and global topology information is limited by the scope of conventional graph Laplacian, leading to unsatisfactory performance on some datasets, particularly on heterophilic graphs. To
Externí odkaz:
http://arxiv.org/abs/2409.09888
Autor:
Luan, Sitao, Lu, Qincheng, Hua, Chenqing, Wang, Xinyu, Zhu, Jiaqi, Chang, Xiao-Wen, Wolf, Guy, Tang, Jian
Over the past decade, Graph Neural Networks (GNNs) have achieved great success on machine learning tasks with relational data. However, recent studies have found that heterophily can cause significant performance degradation of GNNs, especially on no
Externí odkaz:
http://arxiv.org/abs/2409.05755
Autor:
Gao, Bofei, Song, Feifan, Miao, Yibo, Cai, Zefan, Yang, Zhe, Chen, Liang, Hu, Helan, Xu, Runxin, Dong, Qingxiu, Zheng, Ce, Xiao, Wen, Zhang, Ge, Zan, Daoguang, Lu, Keming, Yu, Bowen, Liu, Dayiheng, Cui, Zeyu, Yang, Jian, Sha, Lei, Wang, Houfeng, Sui, Zhifang, Wang, Peiyi, Liu, Tianyu, Chang, Baobao
Large Language Models (LLMs) exhibit remarkably powerful capabilities. One of the crucial factors to achieve success is aligning the LLM's output with human preferences. This alignment process often requires only a small amount of data to efficiently
Externí odkaz:
http://arxiv.org/abs/2409.02795
Conventionally, atomic vapor is perceived as a non-living system governed by the principles of thermodynamics and statistical physics. However, the demarcation line between life and non-life appears to be less distinct than previously thought. In a s
Externí odkaz:
http://arxiv.org/abs/2408.04950
With the emergence of super-resolution lenses such as superlens and hyperlens, coupled with advancements in metamaterials, the diffraction limit of approximately half wavelength is no longer unbreakable. However, superlenses are easily affected by we
Externí odkaz:
http://arxiv.org/abs/2407.19506
Autor:
Luan, Sitao, Hua, Chenqing, Lu, Qincheng, Ma, Liheng, Wu, Lirong, Wang, Xinyu, Xu, Minkai, Chang, Xiao-Wen, Precup, Doina, Ying, Rex, Li, Stan Z., Tang, Jian, Wolf, Guy, Jegelka, Stefanie
Homophily principle, \ie{} nodes with the same labels or similar attributes are more likely to be connected, has been commonly believed to be the main reason for the superiority of Graph Neural Networks (GNNs) over traditional Neural Networks (NNs) o
Externí odkaz:
http://arxiv.org/abs/2407.09618
Autor:
Gao, Bofei, Cai, Zefan, Xu, Runxin, Wang, Peiyi, Zheng, Ce, Lin, Runji, Lu, Keming, Liu, Dayiheng, Zhou, Chang, Xiao, Wen, Hu, Junjie, Liu, Tianyu, Chang, Baobao
In recent progress, mathematical verifiers have achieved success in mathematical reasoning tasks by validating the correctness of solutions generated by policy models. However, existing verifiers are trained with binary classification labels, which a
Externí odkaz:
http://arxiv.org/abs/2406.14024