Zobrazeno 1 - 10
of 662
pro vyhledávání: '"SHEN Yifan"'
Publikováno v:
Zhejiang dianli, Vol 42, Iss 6, Pp 78-85 (2023)
In the context of constructing a new-type power system, the demand response management strategy focusing on virtual power plants participating in power grid scheduling and operation is of great significance. However, most studies now focus on the
Externí odkaz:
https://doaj.org/article/35ef336609ce41e99c5726060016ca7b
Identifying the causal relations between interested variables plays a pivotal role in representation learning as it provides deep insights into the dataset. Identifiability, as the central theme of this approach, normally hinges on leveraging data fr
Externí odkaz:
http://arxiv.org/abs/2408.05788
Autor:
Cai, Ruichu, Jiang, Zhifang, Li, Zijian, Chen, Weilin, Chen, Xuexin, Hao, Zhifeng, Shen, Yifan, Chen, Guangyi, Zhang, Kun
Existing methods for multi-modal time series representation learning aim to disentangle the modality-shared and modality-specific latent variables. Although achieving notable performances on downstream tasks, they usually assume an orthogonal latent
Externí odkaz:
http://arxiv.org/abs/2405.16083
Autor:
Li, Zijian, Shen, Yifan, Zheng, Kaitao, Cai, Ruichu, Song, Xiangchen, Gong, Mingming, Zhu, Zhengmao, Chen, Guangyi, Zhang, Kun
Temporally causal representation learning aims to identify the latent causal process from time series observations, but most methods require the assumption that the latent causal processes do not have instantaneous relations. Although some recent met
Externí odkaz:
http://arxiv.org/abs/2405.15325
Segment Anything Models (SAM) have made significant advancements in image segmentation, allowing users to segment target portions of an image with a single click (i.e., user prompt). Given its broad applications, the robustness of SAM against adversa
Externí odkaz:
http://arxiv.org/abs/2404.08255
Dataset distillation (DD) allows datasets to be distilled to fractions of their original size while preserving the rich distributional information so that models trained on the distilled datasets can achieve a comparable accuracy while saving signifi
Externí odkaz:
http://arxiv.org/abs/2403.10045
Autor:
Li, Zijian, Cai, Ruichu, Yang, Zhenhui, Huang, Haiqin, Chen, Guangyi, Shen, Yifan, Chen, Zhengming, Song, Xiangchen, Zhang, Kun
Temporal distribution shifts are ubiquitous in time series data. One of the most popular methods assumes that the temporal distribution shift occurs uniformly to disentangle the stationary and nonstationary dependencies. But this assumption is diffic
Externí odkaz:
http://arxiv.org/abs/2402.12767
Autor:
Chen, Xuzheng, Zhang, Jie, Fu, Ting, Shen, Yifan, Ma, Shu, Qian, Kun, Zhu, Lingjun, Shi, Chao, Zhang, Yin, Liu, Ming, Wang, Zeke
Network speeds grow quickly in the modern cloud, so SmartNICs are introduced to offload network processing tasks, even application logic. However, typical multicore SmartNICs such as BlueFiled-2 are only capable of processing control-plane tasks with
Externí odkaz:
http://arxiv.org/abs/2402.03041
Publikováno v:
Polish Maritime Research, Vol 24, Iss s3, Pp 102-109 (2017)
Ship stowage plan is the management connection of quae crane scheduling and yard crane scheduling. The quality of ship stowage plan affects the productivity greatly. Previous studies mainly focuses on solving stowage planning problem with online sear
Externí odkaz:
https://doaj.org/article/b037bccdc9e14dab844462928ccec99d
Autor:
Ma, Qun, Xue, Xiao, Zhou, Deyu, Yu, Xiangning, Liu, Donghua, Zhang, Xuwen, Zhao, Zihan, Shen, Yifan, Ji, Peilin, Li, Juanjuan, Wang, Gang, Ma, Wanpeng
Computational experiments have emerged as a valuable method for studying complex systems, involving the algorithmization of counterfactuals. However, accurately representing real social systems in Agent-based Modeling (ABM) is challenging due to the
Externí odkaz:
http://arxiv.org/abs/2402.00262