Zobrazeno 1 - 10
of 1 619
pro vyhledávání: '"LIU LiYuan"'
Publikováno v:
Translational Neuroscience, Vol 14, Iss 1, Pp 1654-63 (2023)
This study aimed to explore the clinical characteristics of acute cerebral infarction (ACI) patients with thalassemia through the analysis of clinical data. Adult patients with ACI who were admitted to the First Affiliated Hospital of Hainan Medical
Externí odkaz:
https://doaj.org/article/3633f250847545e2a37638447e5dd748
Publikováno v:
Jixie qiangdu, Pp 1350-1356 (2022)
In order to meet the requirements of vehicle lightweight and crash safety, flax-basalt fiber composite laminates were made. The tensile, shear and impact resistance properties of the composite structure were discussed through test and simulation anal
Externí odkaz:
https://doaj.org/article/6c88f21cceed4f18bdff499f08772baf
Autor:
Liu Yi, Ouyang Chunmei, Xu Quan, Su Xiaoqiang, Ma Jiajun, Zhao Jing, Li Yanfeng, Tian Zhen, Gu Jianqiang, Liu Liyuan, Han Jiaguang, Zhang Weili
Publikováno v:
Nanophotonics, Vol 11, Iss 9, Pp 1977-1987 (2021)
Hyperbolic metasurfaces with unique dispersion properties can manipulate light–matter interactions according to the demands. However, due to their inherent physical properties, topological transitions (flat bands) exist only in the orthogonal direc
Externí odkaz:
https://doaj.org/article/74f503777f334bbb90a38fdce3477ece
Real-time 2D keypoint detection plays an essential role in computer vision. Although CNN-based and Transformer-based methods have achieved breakthrough progress, they often fail to deliver superior performance and real-time speed. This paper introduc
Externí odkaz:
http://arxiv.org/abs/2412.01422
The prevailing of artificial intelligence-of-things calls for higher energy-efficient edge computing paradigms, such as neuromorphic agents leveraging brain-inspired spiking neural network (SNN) models based on spatiotemporally sparse binary spikes.
Externí odkaz:
http://arxiv.org/abs/2411.11082
Large language models (LLMs) have shown remarkable in-context learning (ICL) capabilities on textual data. We explore whether these capabilities can be extended to continuous vectors from diverse domains, obtained from black-box pretrained encoders.
Externí odkaz:
http://arxiv.org/abs/2410.05629
Autor:
Zhang, Rongzhi, Wang, Kuang, Liu, Liyuan, Wang, Shuohang, Cheng, Hao, Zhang, Chao, Shen, Yelong
The Key-Value (KV) cache is a crucial component in serving transformer-based autoregressive large language models (LLMs), enabling faster inference by storing previously computed KV vectors. However, its memory consumption scales linearly with sequen
Externí odkaz:
http://arxiv.org/abs/2410.03111
Autor:
Liu, Liyuan, Kim, Young Jin, Wang, Shuohang, Liang, Chen, Shen, Yelong, Cheng, Hao, Liu, Xiaodong, Tanaka, Masahiro, Wu, Xiaoxia, Hu, Wenxiang, Chaudhary, Vishrav, Lin, Zeqi, Zhang, Chenruidong, Xue, Jilong, Awadalla, Hany, Gao, Jianfeng, Chen, Weizhu
Mixture-of-Experts (MoE) models scale more effectively than dense models due to sparse computation through expert routing, selectively activating only a small subset of expert modules. However, sparse computation challenges traditional training pract
Externí odkaz:
http://arxiv.org/abs/2409.12136
Autor:
Zhang, Qingru, Yu, Xiaodong, Singh, Chandan, Liu, Xiaodong, Liu, Liyuan, Gao, Jianfeng, Zhao, Tuo, Roth, Dan, Cheng, Hao
Large language models (LLMs) have demonstrated remarkable performance across various real-world tasks. However, they often struggle to fully comprehend and effectively utilize their input contexts, resulting in responses that are unfaithful or halluc
Externí odkaz:
http://arxiv.org/abs/2409.10790
Autor:
Abdin, Marah, Aneja, Jyoti, Awadalla, Hany, Awadallah, Ahmed, Awan, Ammar Ahmad, Bach, Nguyen, Bahree, Amit, Bakhtiari, Arash, Bao, Jianmin, Behl, Harkirat, Benhaim, Alon, Bilenko, Misha, Bjorck, Johan, Bubeck, Sébastien, Cai, Martin, Cai, Qin, Chaudhary, Vishrav, Chen, Dong, Chen, Dongdong, Chen, Weizhu, Chen, Yen-Chun, Chen, Yi-Ling, Cheng, Hao, Chopra, Parul, Dai, Xiyang, Dixon, Matthew, Eldan, Ronen, Fragoso, Victor, Gao, Jianfeng, Gao, Mei, Gao, Min, Garg, Amit, Del Giorno, Allie, Goswami, Abhishek, Gunasekar, Suriya, Haider, Emman, Hao, Junheng, Hewett, Russell J., Hu, Wenxiang, Huynh, Jamie, Iter, Dan, Jacobs, Sam Ade, Javaheripi, Mojan, Jin, Xin, Karampatziakis, Nikos, Kauffmann, Piero, Khademi, Mahoud, Kim, Dongwoo, Kim, Young Jin, Kurilenko, Lev, Lee, James R., Lee, Yin Tat, Li, Yuanzhi, Li, Yunsheng, Liang, Chen, Liden, Lars, Lin, Xihui, Lin, Zeqi, Liu, Ce, Liu, Liyuan, Liu, Mengchen, Liu, Weishung, Liu, Xiaodong, Luo, Chong, Madan, Piyush, Mahmoudzadeh, Ali, Majercak, David, Mazzola, Matt, Mendes, Caio César Teodoro, Mitra, Arindam, Modi, Hardik, Nguyen, Anh, Norick, Brandon, Patra, Barun, Perez-Becker, Daniel, Portet, Thomas, Pryzant, Reid, Qin, Heyang, Radmilac, Marko, Ren, Liliang, de Rosa, Gustavo, Rosset, Corby, Roy, Sambudha, Ruwase, Olatunji, Saarikivi, Olli, Saied, Amin, Salim, Adil, Santacroce, Michael, Shah, Shital, Shang, Ning, Sharma, Hiteshi, Shen, Yelong, Shukla, Swadheen, Song, Xia, Tanaka, Masahiro, Tupini, Andrea, Vaddamanu, Praneetha, Wang, Chunyu, Wang, Guanhua, Wang, Lijuan, Wang, Shuohang, Wang, Xin, Wang, Yu, Ward, Rachel, Wen, Wen, Witte, Philipp, Wu, Haiping, Wu, Xiaoxia, Wyatt, Michael, Xiao, Bin, Xu, Can, Xu, Jiahang, Xu, Weijian, Xue, Jilong, Yadav, Sonali, Yang, Fan, Yang, Jianwei, Yang, Yifan, Yang, Ziyi, Yu, Donghan, Yuan, Lu, Zhang, Chenruidong, Zhang, Cyril, Zhang, Jianwen, Zhang, Li Lyna, Zhang, Yi, Zhang, Yue, Zhang, Yunan, Zhou, Xiren
We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi
Externí odkaz:
http://arxiv.org/abs/2404.14219