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pro vyhledávání: '"Wang, April"'
Autor:
Oney, Steve, Shen, Yue, Wu, Fei, Hong, Young Suh, Wang, Ziang, Khajekar, Yamini, Zhang, Jiacheng, Wang, April Yi
Large Language Models (LLMs) have shown the potential to be valuable teaching tools, with the potential of giving every student a personalized tutor. However, one challenge with using LLMs to learn new concepts is that when learning a topic in an unf
Externí odkaz:
http://arxiv.org/abs/2411.10687
Autor:
Zhu, Qian, Wang, Dakuo, Ma, Shuai, Wang, April Yi, Chen, Zixin, Khurana, Udayan, Ma, Xiaojuan
As AI technology continues to advance, the importance of human-AI collaboration becomes increasingly evident, with numerous studies exploring its potential in various fields. One vital field is data science, including feature engineering (FE), where
Externí odkaz:
http://arxiv.org/abs/2405.14107
Real-time collaborative editing in computational notebooks can improve the efficiency of teamwork for data scientists. However, working together through synchronous editing of notebooks introduces new challenges. Data scientists may inadvertently int
Externí odkaz:
http://arxiv.org/abs/2404.04695
We present TetGAN, a convolutional neural network designed to generate tetrahedral meshes. We represent shapes using an irregular tetrahedral grid which encodes an occupancy and displacement field. Our formulation enables defining tetrahedral convolu
Externí odkaz:
http://arxiv.org/abs/2210.05735
Creating presentation slides is a critical but time-consuming task for data scientists. While researchers have proposed many AI techniques to lift data scientists' burden on data preparation and model selection, few have targeted the presentation cre
Externí odkaz:
http://arxiv.org/abs/2203.11085
Human-Centered AI (HCAI) refers to the research effort that aims to design and implement AI techniques to support various human tasks, while taking human needs into consideration and preserving human control. In this short position paper, we illustra
Externí odkaz:
http://arxiv.org/abs/2110.01108
Jupyter notebook allows data scientists to write machine learning code together with its documentation in cells. In this paper, we propose a new task of code documentation generation (CDG) for computational notebooks. In contrast to the previous CDG
Externí odkaz:
http://arxiv.org/abs/2104.01002
Autor:
Wang, April Yi, Wang, Dakuo, Drozdal, Jaimie, Muller, Michael, Park, Soya, Weisz, Justin D., Liu, Xuye, Wu, Lingfei, Dugan, Casey
Publikováno v:
ACM Trans. Comput.-Hum. Interact. 29, 2, Article 17 (April 2022), 33 pages
Computational notebooks allow data scientists to express their ideas through a combination of code and documentation. However, data scientists often pay attention only to the code, and neglect creating or updating their documentation during quick ite
Externí odkaz:
http://arxiv.org/abs/2102.12592
Data scientists face a steep learning curve in understanding a new domain for which they want to build machine learning (ML) models. While input from domain experts could offer valuable help, such input is often limited, expensive, and generally not
Externí odkaz:
http://arxiv.org/abs/2102.00036
The development of AI applications is a multidisciplinary effort, involving multiple roles collaborating with the AI developers, an umbrella term we use to include data scientists and other AI-adjacent roles on the same team. During these collaborati
Externí odkaz:
http://arxiv.org/abs/2101.06098