Zobrazeno 1 - 10
of 3 032
pro vyhledávání: '"ZHANG Yulong"'
Autor:
HOU Yuna, ZHANG Yulong
Publikováno v:
Jiaoshi jiaoyu xuebao, Vol 11, Iss 2, Pp 121-130 (2024)
Teachers' continuous professional development across different school stages plays a crucial role in children's sustainable development and the construction of a national high-quality education system. OECD countries have actively explored the contin
Externí odkaz:
https://doaj.org/article/7619d4350d6a4c4a9c6f2ead4fbf3c45
Autor:
ZHANG Pengming, ZHANG Yulong, WANG Dapeng, HAN Changjiang, WANG Junfeng, HAO Zijing, ZHOU Chunshan
Publikováno v:
Meikuang Anquan, Vol 54, Iss 1, Pp 46-55 (2023)
In order to prevent and control the emission of toxic and harmful gases in the goaf of close coal seams, it is of great significance to study its distribution and migration behavior. Taking 1818 working face of Jinshen Shaping Coal Mine in Shanxi Pro
Externí odkaz:
https://doaj.org/article/9f6b520acd9f4af59e5f663261d68e98
Publikováno v:
Guan'gai paishui xuebao, Vol 40, Iss 12, Pp 70-77 (2021)
【Objective】 Organic fertilization and soil conditioner have been found capable of improving soil structure and enzymatic activity. The aim of this paper is to study how organic fertilizer and soil conditioner combine to regulate bioavailable nitr
Externí odkaz:
https://doaj.org/article/fc663364be764d9cb686107fe3a83047
Low-Rank Adaptation (LoRA) is a parameter-efficient technique for rapidly fine-tuning foundation models. In standard LoRA training dynamics, models tend to quickly converge to a local optimum near the initialization. However, this local optimum may n
Externí odkaz:
http://arxiv.org/abs/2410.22911
Autor:
Yu, Tianyuan, Ma, Xinyu, Patil, Varun, Kocaogullar, Yekta, Zhang, Yulong, Burke, Jeff, Kutscher, Dirk, Zhang, Lixia
This position paper explores how to support the Web's evolution through an underlying data-centric approach that better matches the data-orientedness of modern and emerging applications. We revisit the original vision of the Web as a hypermedia syste
Externí odkaz:
http://arxiv.org/abs/2407.15221
Empirical Risk Minimization (ERM) is fragile in scenarios with insufficient labeled samples. A vanilla extension of ERM to unlabeled samples is Entropy Minimization (EntMin), which employs the soft-labels of unlabeled samples to guide their learning.
Externí odkaz:
http://arxiv.org/abs/2406.02862
Recent advances achieved by deep learning models rely on the independent and identically distributed assumption, hindering their applications in real-world scenarios with domain shifts. To tackle this issue, cross-domain learning aims at extracting d
Externí odkaz:
http://arxiv.org/abs/2401.03253
Limited transferability hinders the performance of deep learning models when applied to new application scenarios. Recently, Unsupervised Domain Adaptation (UDA) has achieved significant progress in addressing this issue via learning domain-invariant
Externí odkaz:
http://arxiv.org/abs/2309.14360
Information-Centric Networking (ICN), with its data-oriented operation and generally more powerful forwarding layer, provides an attractive platform for distributed computing. This paper provides a systematic overview and categorization of different
Externí odkaz:
http://arxiv.org/abs/2309.08973
Limited transferability hinders the performance of deep learning models when applied to new application scenarios. Recently, unsupervised domain adaptation (UDA) has achieved significant progress in addressing this issue via learning domain-invariant
Externí odkaz:
http://arxiv.org/abs/2303.12724