Zobrazeno 1 - 10
of 671
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
Publikováno v:
能源环境保护, Vol 37, Iss 2, Pp 98-105 (2023)
The industry baseline method is adopted to allocate carbon allowances in China's national carbon market, neither considering its huge inner regional disparity, nor exhibiting enough flexibility to promote renewable energy. The origination, developmen
Externí odkaz:
https://doaj.org/article/a091d0e367ee4c60a1efc5ac1adc55cc
Autor:
Shen Yifan
Publikováno v:
SHS Web of Conferences, Vol 199, p 01002 (2024)
Although the conflict between traditional assessment methods and personalised education has gained wide attention, significant gaps remain in understanding their reconciliation, especially concerning their contribution to the students' further academ
Externí odkaz:
https://doaj.org/article/712122c990ec450b886c19bc3e2b68d8
Autor:
Shen, Yifan, Yarkony, David
We present a symmetry adapted residual neural network (SAResNet) diabatization method to construct quasi-diabatic Hamiltonians that accurately represent ab initio adiabatic energies, energy gradients, and nonadiabatic couplings for moderate sized sys
Externí odkaz:
http://arxiv.org/abs/2411.01702
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