Zobrazeno 1 - 10
of 296
pro vyhledávání: '"Zhan, Xueying"'
As deep learning continues to evolve, the need for data efficiency becomes increasingly important. Considering labeling large datasets is both time-consuming and expensive, active learning (AL) provides a promising solution to this challenge by itera
Externí odkaz:
http://arxiv.org/abs/2410.13853
Cryo-electron microscopy (cryo-EM) emerges as a pivotal technology for determining the architecture of cells, viruses, and protein assemblies at near-atomic resolution. Traditional particle picking, a key step in cryo-EM, struggles with manual effort
Externí odkaz:
http://arxiv.org/abs/2404.10178
Autor:
Zhan, Xueying, Dai, Zeyu, Wang, Qingzhong, Li, Qing, Xiong, Haoyi, Dou, Dejing, Chan, Antoni B.
Pool-based Active Learning (AL) has achieved great success in minimizing labeling cost by sequentially selecting informative unlabeled samples from a large unlabeled data pool and querying their labels from oracle/annotators. However, existing AL sam
Externí odkaz:
http://arxiv.org/abs/2207.01190
Autor:
Li, Xingjian, Yang, Pengkun, Gu, Yangcheng, Zhan, Xueying, Wang, Tianyang, Xu, Min, Xu, Chengzhong
Uncertainty estimation for unlabeled data is crucial to active learning. With a deep neural network employed as the backbone model, the data selection process is highly challenging due to the potential over-confidence of the model inference. Existing
Externí odkaz:
http://arxiv.org/abs/2205.13340
While deep learning (DL) is data-hungry and usually relies on extensive labeled data to deliver good performance, Active Learning (AL) reduces labeling costs by selecting a small proportion of samples from unlabeled data for labeling and training. Th
Externí odkaz:
http://arxiv.org/abs/2203.13450
Active learning aims to achieve greater accuracy with less training data by selecting the most useful data samples from which it learns. Single-criterion based methods (i.e., informativeness and representativeness based methods) are simple and effici
Externí odkaz:
http://arxiv.org/abs/2107.01622
Autor:
Zhan, Xueying, Chan, Antoni Bert
Active learning (AL) is a subfield of machine learning (ML) in which a learning algorithm could achieve good accuracy with less training samples by interactively querying a user/oracle to label new data points. Pool-based AL is well-motivated in many
Externí odkaz:
http://arxiv.org/abs/2010.08161
Autor:
Yan, Shan, Yang, Jia, Cai, Yuchen, Wang, Yanrong, Li, Shuhui, Zhan, Xueying, Wang, Feng, He, Jun, Wang, Zhenxing
Publikováno v:
Small; 10/3/2024, Vol. 20 Issue 40, p1-8, 8p
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.