Zobrazeno 1 - 10
of 417
pro vyhledávání: '"SONG Minghui"'
Autor:
WANG Shujuan, FAN Yiling, FENG Zhen, JIANG Bo, SONG Minghui, LI Qiongqiong, LIU Hao, QIN Feng, YANG Meicheng
Publikováno v:
Shanghai yufang yixue, Vol 34, Iss 6, Pp 511-518 (2022)
ObjectiveA rapid enrichment and detection method for Escherichia coli O157∶H7 was developed by using multienzyme isothermal rapid amplification (MIRA) fluorescence method combined with metal organic frameworks immunomagnetic beads.MethodsUsing
Externí odkaz:
https://doaj.org/article/692b2dc1ac3d46c2aca813b183f41785
Autor:
Jiang, Ting, Song, Minghui, Zhang, Zihan, Huang, Haizhen, Deng, Weiwei, Sun, Feng, Zhang, Qi, Wang, Deqing, Zhuang, Fuzhen
Multimodal large language models (MLLMs) have shown promising advancements in general visual and language understanding. However, the representation of multimodal information using MLLMs remains largely unexplored. In this work, we introduce a new fr
Externí odkaz:
http://arxiv.org/abs/2407.12580
Autor:
Liu, Yuxuan, Yang, Tianchi, Zhang, Zihan, Song, Minghui, Huang, Haizhen, Deng, Weiwei, Sun, Feng, Zhang, Qi
Generative retrieval, a promising new paradigm in information retrieval, employs a seq2seq model to encode document features into parameters and decode relevant document identifiers (IDs) based on search queries. Existing generative retrieval solutio
Externí odkaz:
http://arxiv.org/abs/2405.14280
This paper proposes an adaptive numerical method for stochastic delay differential equations (SDDEs) with a non-global Lipschitz drift term and a non-constant delay, building upon the work of Wei Fang and others. The method adapts the step size based
Externí odkaz:
http://arxiv.org/abs/2404.10244
Autor:
Shi, Shuhua, Huang, Shaohan, Song, Minghui, Li, Zhoujun, Zhang, Zihan, Huang, Haizhen, Wei, Furu, Deng, Weiwei, Sun, Feng, Zhang, Qi
As one of the most popular parameter-efficient fine-tuning (PEFT) methods, low-rank adaptation (LoRA) is commonly applied to fine-tune large language models (LLMs). However, updating the weights of LoRA blocks effectively and expeditiously is challen
Externí odkaz:
http://arxiv.org/abs/2402.18039
Autor:
Wang, Zhaoyang, Huang, Shaohan, Liu, Yuxuan, Wang, Jiahai, Song, Minghui, Zhang, Zihan, Huang, Haizhen, Wei, Furu, Deng, Weiwei, Sun, Feng, Zhang, Qi
Large language models (LLMs) exhibit impressive emergent abilities in natural language processing, but their democratization is hindered due to huge computation requirements and closed-source nature. Recent research on advancing open-source smaller L
Externí odkaz:
http://arxiv.org/abs/2310.13332
Autor:
Yang, Tianchi, Song, Minghui, Zhang, Zihan, Huang, Haizhen, Deng, Weiwei, Sun, Feng, Zhang, Qi
Generative retrieval, which is a new advanced paradigm for document retrieval, has recently attracted research interests, since it encodes all documents into the model and directly generates the retrieved documents. However, its power is still underu
Externí odkaz:
http://arxiv.org/abs/2310.12455
This work deals with the Euler-Maruyama (EM) scheme for stochastic differential equations with Markovian switching (SDEwMSs). We focus on the Lp-convergence rate (p is greater than or equal to 2) of the EM method given in this paper. As far as we kno
Externí odkaz:
http://arxiv.org/abs/2208.13174
In this paper, we consider the equivalence of the $p$th moment exponential stability for stochastic differential equations (SDEs), stochastic differential equations with piecewise continuous arguments (SDEPCAs) and the corresponding Euler-Maruyama me
Externí odkaz:
http://arxiv.org/abs/2001.05203
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.