ChartifyText: Automated Chart Generation from Data-Involved Texts via LLM

Autor: Zhang, Songheng, Wang, Lei, Li, Toby Jia-Jun, Shen, Qiaomu, Cao, Yixin, Wang, Yong

Text documents with numerical values involved are widely used in various applications such as scientific research, economy, public health and journalism. However, it is difficult for readers to quickly interpret such data-involved texts and gain deep

Externí odkaz: http://arxiv.org/abs/2410.14331

ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning

Autor: Wang, Qianwen, Ming, Yao, Jin, Zhihua, Shen, Qiaomu, Liu, Dongyu, Smith, Micah J., Veeramachaneni, Kalyan, Qu, Huamin

Publikováno v: In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19). Association for Computing Machinery, New York, NY, USA, Paper 681, 1-12

To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge model search space, it is impos

Externí odkaz: http://arxiv.org/abs/1902.05009

$\mathsf {CheetahTraj}$CheetahTraj: Efficient Visualization for Large Trajectory Dataset With Quality Guarantee

Autor: Shen, Qiaomu, Zhang, Chaozu, Yan, Xiao, Yang, Chuan, Zeng, Dan, Zeng, Wei, Tang, Bo

Publikováno v: IEEE Transactions on Knowledge and Data Engineering; November 2024, Vol. 36 Issue: 11 p5737-5752, 16p

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