Zobrazeno 1 - 10
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pro vyhledávání: '"ZHANG, Tianshu"'
Autor:
Meng, Shiqiao, Zhou, Ying, Zheng, Qinghua, Liao, Bingxu, Chang, Mushi, Zhang, Tianshu, Djerrad, Abderrahim
Accurately predicting the dynamic responses of building structures under seismic loads is essential for ensuring structural safety and minimizing potential damage. This critical aspect of structural analysis allows engineers to evaluate how structure
Externí odkaz:
http://arxiv.org/abs/2410.20186
Autor:
Feng, Jie, Zhang, Tianshu, Zhang, Junpeng, Shang, Ronghua, Dong, Weisheng, Shi, Guangming, Jiao, Licheng
Unsupervised domain adaptation techniques, extensively studied in hyperspectral image (HSI) classification, aim to use labeled source domain data and unlabeled target domain data to learn domain invariant features for cross-scene classification. Comp
Externí odkaz:
http://arxiv.org/abs/2408.15263
Multi-modal (vision-language) models, such as CLIP, are replacing traditional supervised pre-training models (e.g., ImageNet-based pre-training) as the new generation of visual foundation models. These models with robust and aligned semantic represen
Externí odkaz:
http://arxiv.org/abs/2401.01736
Semi-structured tables are ubiquitous. There has been a variety of tasks that aim to automatically interpret, augment, and query tables. Current methods often require pretraining on tables or special model architecture design, are restricted to speci
Externí odkaz:
http://arxiv.org/abs/2311.09206
Autor:
Mo, Lingbo, Chen, Shijie, Chen, Ziru, Deng, Xiang, Lewis, Ashley, Singh, Sunit, Stevens, Samuel, Tai, Chang-You, Wang, Zhen, Yue, Xiang, Zhang, Tianshu, Su, Yu, Sun, Huan
We introduce TacoBot, a user-centered task-oriented digital assistant designed to guide users through complex real-world tasks with multiple steps. Covering a wide range of cooking and how-to tasks, we aim to deliver a collaborative and engaging dial
Externí odkaz:
http://arxiv.org/abs/2307.16081
This paper studies a new task of federated learning (FL) for semantic parsing, where multiple clients collaboratively train one global model without sharing their semantic parsing data. By leveraging data from multiple clients, the FL paradigm can be
Externí odkaz:
http://arxiv.org/abs/2305.17221
In-context learning with large language models (LLMs) has recently caught increasing attention due to its superior few-shot performance on various tasks. However, its performance on text-to-SQL parsing still has much room for improvement. In this pap
Externí odkaz:
http://arxiv.org/abs/2305.14215
Autor:
Zhang, Tianshu1,2 (AUTHOR) zhangtianshu2000@163.com, Yuan, Yongda1,2 (AUTHOR) yuanyongda@saas.sh.cn, Teng, Haiyuan1,2 (AUTHOR) tenghaiyuan@126.com, Wang, Dongsheng1,2 (AUTHOR) zb3@saas.sh.cn, Gu, Haotian1,2 (AUTHOR) guhaotian@saas.sh.cn
Publikováno v:
Insects (2075-4450). Oct2024, Vol. 15 Issue 10, p773. 14p.
Autor:
Chen, Shijie, Chen, Ziru, Deng, Xiang, Lewis, Ashley, Mo, Lingbo, Stevens, Samuel, Wang, Zhen, Yue, Xiang, Zhang, Tianshu, Su, Yu, Sun, Huan
We present TacoBot, a task-oriented dialogue system built for the inaugural Alexa Prize TaskBot Challenge, which assists users in completing multi-step cooking and home improvement tasks. TacoBot is designed with a user-centered principle and aspires
Externí odkaz:
http://arxiv.org/abs/2207.05223
Publikováno v:
In Atmospheric Environment 15 November 2024 337