Zobrazeno 1 - 10
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pro vyhledávání: '"zhang, Jinming"'
Autor:
Zhang, Jinming, Long, Yunfei
Narrative understanding and story generation are critical challenges in natural language processing (NLP), with much of the existing research focused on summarization and question-answering tasks. While previous studies have explored predicting plot
Externí odkaz:
http://arxiv.org/abs/2412.02897
This study investigates differential item functioning (DIF) detection in computerized adaptive testing (CAT) using multilevel modeling. We argue that traditional DIF methods have proven ineffective in CAT due to the hierarchical nature of the data. O
Externí odkaz:
http://arxiv.org/abs/2409.16534
Autor:
Zhou, Xuanru, Lian, Jiachen, Cho, Cheol Jun, Liu, Jingwen, Ye, Zongli, Zhang, Jinming, Morin, Brittany, Baquirin, David, Vonk, Jet, Ezzes, Zoe, Miller, Zachary, Tempini, Maria Luisa Gorno, Anumanchipalli, Gopala
Speech dysfluency modeling is a task to detect dysfluencies in speech, such as repetition, block, insertion, replacement, and deletion. Most recent advancements treat this problem as a time-based object detection problem. In this work, we revisit thi
Externí odkaz:
http://arxiv.org/abs/2409.13582
Autor:
Kaptur, Dandan Chen, Liu, Yiqing, Kaptur, Bradley, Peterman, Nicholas, Zhang, Jinming, Kern, Justin, Anderson, Carolyn
Few health-related constructs or measures have received critical evaluation in terms of measurement equivalence, such as self-reported health survey data. Differential item functioning (DIF) analysis is crucial for evaluating measurement equivalence
Externí odkaz:
http://arxiv.org/abs/2408.13702
Autor:
Kaptur, Dandan Chen, Zhang, Jinming
This study evaluated four multi-group differential item functioning (DIF) methods (the root mean square deviation approach, Wald-1, generalized logistic regression procedure, and generalized Mantel-Haenszel method) via Monte Carlo simulation of contr
Externí odkaz:
http://arxiv.org/abs/2408.11922
Autor:
Yang, Lin, Yuan, Haibo, Duan, Fuqing, Zhang, Ruoyi, Huang, Bowen, Xiao, Kai, Xu, Shuai, Zhang, Jinming
The upcoming Chinese Space Station Telescope (CSST) slitless spectroscopic survey poses a challenge of flux calibration, which requires a large number of flux-standard stars. In this work, we design an uncertainty-aware residual attention network, th
Externí odkaz:
http://arxiv.org/abs/2401.13167
Point-cloud-based 3D perception has attracted great attention in various applications including robotics, autonomous driving and AR/VR. In particular, the 3D sparse convolution (SpConv) network has emerged as one of the most popular backbones due to
Externí odkaz:
http://arxiv.org/abs/2308.09249
Domain generalization (DG) tends to alleviate the poor generalization capability of deep neural networks by learning model with multiple source domains. A classical solution to DG is domain augmentation, the common belief of which is that diversifyin
Externí odkaz:
http://arxiv.org/abs/2303.13297
Autor:
Polak, Maciej P., Modi, Shrey, Latosinska, Anna, Zhang, Jinming, Wang, Ching-Wen, Wang, Shaonan, Hazra, Ayan Deep, Morgan, Dane
Publikováno v:
Digital Discovery, 2024, 3, 1221-1235
Accurate and comprehensive material databases extracted from research papers are crucial for materials science and engineering, but their development requires significant human effort. With large language models (LLMs) transforming the way humans int
Externí odkaz:
http://arxiv.org/abs/2302.04914
Extensive studies on Unsupervised Domain Adaptation (UDA) have propelled the deployment of deep learning from limited experimental datasets into real-world unconstrained domains. Most UDA approaches align features within a common embedding space and
Externí odkaz:
http://arxiv.org/abs/2208.01195