Zobrazeno 1 - 10
of 146
pro vyhledávání: '"Liu, Yingru"'
Publikováno v:
In Cellular Signalling December 2024 124
Autor:
Zhao, Yaqi, Yang, Wenfang, Liu, Yingru, Zhang, Xuemei, Li, Yanli, Qi, Guohui, Huang, Shaohui, Luan, Haoan
Publikováno v:
In European Journal of Soil Biology June 2024 121
Image captioning is shown to be able to achieve a better performance by using scene graphs to represent the relations of objects in the image. The current captioning encoders generally use a Graph Convolutional Net (GCN) to represent the relation inf
Externí odkaz:
http://arxiv.org/abs/2107.14178
The marriage of recurrent neural networks and neural ordinary differential networks (ODE-RNN) is effective in modeling irregularly-observed sequences. While ODE produces the smooth hidden states between observation intervals, the RNN will trigger a h
Externí odkaz:
http://arxiv.org/abs/2010.01381
Autor:
Yang, Xuewen, Zhang, Heming, Jin, Di, Liu, Yingru, Wu, Chi-Hao, Tan, Jianchao, Xie, Dongliang, Wang, Jue, Wang, Xin
Generating accurate descriptions for online fashion items is important not only for enhancing customers' shopping experiences, but also for the increase of online sales. Besides the need of correctly presenting the attributes of items, the expression
Externí odkaz:
http://arxiv.org/abs/2008.02693
Learning continuous-time stochastic dynamics is a fundamental and essential problem in modeling sporadic time series, whose observations are irregular and sparse in both time and dimension. For a given system whose latent states and observed data are
Externí odkaz:
http://arxiv.org/abs/2006.06145
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Xie, Shipeng, Huang, Guanmin, Liu, Yingru, Guo, Yuling, Peng, Chuanxi, Li, Zhaohu, Zhou, Yuyi, Duan, Liusheng
Publikováno v:
In Environmental and Experimental Botany November 2023 215
Autor:
Liu, Yingru, Yang, Xuewen, Xie, Dongliang, Wang, Xin, Shen, Li, Huang, Haozhi, Balasubramanian, Niranjan
Multi-task learning (MTL) is a common paradigm that seeks to improve the generalization performance of task learning by training related tasks simultaneously. However, it is still a challenging problem to search the flexible and accurate architecture
Externí odkaz:
http://arxiv.org/abs/1911.08065
Learning target side syntactic structure has been shown to improve Neural Machine Translation (NMT). However, incorporating syntax through latent variables introduces additional complexity in inference, as the models need to marginalize over the late
Externí odkaz:
http://arxiv.org/abs/1908.11782