Zobrazeno 1 - 10
of 17 513
pro vyhledávání: '"A. Deli"'
Autor:
Bongratz, Fabian, Karmann, Markus, Holz, Adrian, Bonhoeffer, Moritz, Neumaier, Viktor, Deli, Sarah, Schmitz-Koep, Benita, Zimmer, Claus, Sorg, Christian, Thalhammer, Melissa, Hedderich, Dennis M, Wachinger, Christian
Meningeal lymphatic vessels (MLVs) are responsible for the drainage of waste products from the human brain. An impairment in their functionality has been associated with aging as well as brain disorders like multiple sclerosis and Alzheimer's disease
Externí odkaz:
http://arxiv.org/abs/2411.08537
This paper investigates the information-theoretic energy efficiency of intelligent reflecting surface (IRS)-aided wireless communication systems, taking into account the statistical quality-of-service (QoS) constraints on delay violation probabilitie
Externí odkaz:
http://arxiv.org/abs/2411.00344
Autor:
Cheng, Zesen, Zhang, Hang, Li, Kehan, Leng, Sicong, Hu, Zhiqiang, Wu, Fei, Zhao, Deli, Li, Xin, Bing, Lidong
Contrastive loss is a powerful approach for representation learning, where larger batch sizes enhance performance by providing more negative samples to better distinguish between similar and dissimilar data. However, scaling batch sizes is constraine
Externí odkaz:
http://arxiv.org/abs/2410.17243
Autor:
Li, Long, Xu, Weiwen, Guo, Jiayan, Zhao, Ruochen, Li, Xingxuan, Yuan, Yuqian, Zhang, Boqiang, Jiang, Yuming, Xin, Yifei, Dang, Ronghao, Zhao, Deli, Rong, Yu, Feng, Tian, Bing, Lidong
Effective research ideation is a critical step for scientific research. However, the exponential increase in scientific literature makes it challenging for researchers to stay current with recent advances and identify meaningful research directions.
Externí odkaz:
http://arxiv.org/abs/2410.13185
Autor:
Leng, Sicong, Xing, Yun, Cheng, Zesen, Zhou, Yang, Zhang, Hang, Li, Xin, Zhao, Deli, Lu, Shijian, Miao, Chunyan, Bing, Lidong
Recent advancements in large multimodal models (LMMs) have significantly enhanced performance across diverse tasks, with ongoing efforts to further integrate additional modalities such as video and audio. However, most existing LMMs remain vulnerable
Externí odkaz:
http://arxiv.org/abs/2410.12787
Parameter-efficient fine-tuning (PEFT) is crucial for customizing Large Language Models (LLMs) with constrained resources. Although there have been various PEFT methods for dense-architecture LLMs, PEFT for sparse-architecture LLMs is still underexpl
Externí odkaz:
http://arxiv.org/abs/2407.01906
Autor:
DeepSeek-AI, Zhu, Qihao, Guo, Daya, Shao, Zhihong, Yang, Dejian, Wang, Peiyi, Xu, Runxin, Wu, Y., Li, Yukun, Gao, Huazuo, Ma, Shirong, Zeng, Wangding, Bi, Xiao, Gu, Zihui, Xu, Hanwei, Dai, Damai, Dong, Kai, Zhang, Liyue, Piao, Yishi, Gou, Zhibin, Xie, Zhenda, Hao, Zhewen, Wang, Bingxuan, Song, Junxiao, Chen, Deli, Xie, Xin, Guan, Kang, You, Yuxiang, Liu, Aixin, Du, Qiushi, Gao, Wenjun, Lu, Xuan, Chen, Qinyu, Wang, Yaohui, Deng, Chengqi, Li, Jiashi, Zhao, Chenggang, Ruan, Chong, Luo, Fuli, Liang, Wenfeng
We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoin
Externí odkaz:
http://arxiv.org/abs/2406.11931
Autor:
Cheng, Zesen, Leng, Sicong, Zhang, Hang, Xin, Yifei, Li, Xin, Chen, Guanzheng, Zhu, Yongxin, Zhang, Wenqi, Luo, Ziyang, Zhao, Deli, Bing, Lidong
In this paper, we present the VideoLLaMA 2, a set of Video Large Language Models (Video-LLMs) designed to enhance spatial-temporal modeling and audio understanding in video and audio-oriented tasks. Building upon its predecessor, VideoLLaMA 2 incorpo
Externí odkaz:
http://arxiv.org/abs/2406.07476
As LLMs continuously evolve, there is an urgent need for a reliable evaluation method that delivers trustworthy results promptly. Currently, static benchmarks suffer from inflexibility and unreliability, leading users to prefer human voting platforms
Externí odkaz:
http://arxiv.org/abs/2405.20267
Autor:
DeepSeek-AI, Liu, Aixin, Feng, Bei, Wang, Bin, Wang, Bingxuan, Liu, Bo, Zhao, Chenggang, Dengr, Chengqi, Ruan, Chong, Dai, Damai, Guo, Daya, Yang, Dejian, Chen, Deli, Ji, Dongjie, Li, Erhang, Lin, Fangyun, Luo, Fuli, Hao, Guangbo, Chen, Guanting, Li, Guowei, Zhang, H., Xu, Hanwei, Yang, Hao, Zhang, Haowei, Ding, Honghui, Xin, Huajian, Gao, Huazuo, Li, Hui, Qu, Hui, Cai, J. L., Liang, Jian, Guo, Jianzhong, Ni, Jiaqi, Li, Jiashi, Chen, Jin, Yuan, Jingyang, Qiu, Junjie, Song, Junxiao, Dong, Kai, Gao, Kaige, Guan, Kang, Wang, Lean, Zhang, Lecong, Xu, Lei, Xia, Leyi, Zhao, Liang, Zhang, Liyue, Li, Meng, Wang, Miaojun, Zhang, Mingchuan, Zhang, Minghua, Tang, Minghui, Li, Mingming, Tian, Ning, Huang, Panpan, Wang, Peiyi, Zhang, Peng, Zhu, Qihao, Chen, Qinyu, Du, Qiushi, Chen, R. J., Jin, R. L., Ge, Ruiqi, Pan, Ruizhe, Xu, Runxin, Chen, Ruyi, Li, S. S., Lu, Shanghao, Zhou, Shangyan, Chen, Shanhuang, Wu, Shaoqing, Ye, Shengfeng, Ma, Shirong, Wang, Shiyu, Zhou, Shuang, Yu, Shuiping, Zhou, Shunfeng, Zheng, Size, Wang, T., Pei, Tian, Yuan, Tian, Sun, Tianyu, Xiao, W. L., Zeng, Wangding, An, Wei, Liu, Wen, Liang, Wenfeng, Gao, Wenjun, Zhang, Wentao, Li, X. Q., Jin, Xiangyue, Wang, Xianzu, Bi, Xiao, Liu, Xiaodong, Wang, Xiaohan, Shen, Xiaojin, Chen, Xiaokang, Chen, Xiaosha, Nie, Xiaotao, Sun, Xiaowen, Wang, Xiaoxiang, Liu, Xin, Xie, Xin, Yu, Xingkai, Song, Xinnan, Zhou, Xinyi, Yang, Xinyu, Lu, Xuan, Su, Xuecheng, Wu, Y., Li, Y. K., Wei, Y. X., Zhu, Y. X., Xu, Yanhong, Huang, Yanping, Li, Yao, Zhao, Yao, Sun, Yaofeng, Li, Yaohui, Wang, Yaohui, Zheng, Yi, Zhang, Yichao, Xiong, Yiliang, Zhao, Yilong, He, Ying, Tang, Ying, Piao, Yishi, Dong, Yixin, Tan, Yixuan, Liu, Yiyuan, Wang, Yongji, Guo, Yongqiang, Zhu, Yuchen, Wang, Yuduan, Zou, Yuheng, Zha, Yukun, Ma, Yunxian, Yan, Yuting, You, Yuxiang, Liu, Yuxuan, Ren, Z. Z., Ren, Zehui, Sha, Zhangli, Fu, Zhe, Huang, Zhen, Zhang, Zhen, Xie, Zhenda, Hao, Zhewen, Shao, Zhihong, Wen, Zhiniu, Xu, Zhipeng, Zhang, Zhongyu, Li, Zhuoshu, Wang, Zihan, Gu, Zihui, Li, Zilin, Xie, Ziwei
We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token, and supports a context length of 128
Externí odkaz:
http://arxiv.org/abs/2405.04434