most citedChatPipe: Orchestrating Data Preparation Program by Optimizing Human-ChatGPT Interactions

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024

A Two-Phase Recall-and-Select Framework for Fast Model Selection

Jianwei Cui, Wenhang Shi, Honglin Tao +2

As the ubiquity of deep learning in various machine learning applications has amplified, a proliferation of neural network models has been trained and shared on public model reposi…

cs.DC2024

Lion: Minimizing Distributed Transactions through Adaptive Replica Provision (Extended Version)

Qiushi Zheng, Zhanhao Zhao, Wei Lu +4

Distributed transaction processing often involves multiple rounds of cross-node communications, and therefore tends to be slow. To improve performance, existing approaches convert…

cs.DC2024

Xorbits: Automating Operator Tiling for Distributed Data Science

Weizheng Lu, Kaisheng He, Xuye Qin +7

Data science pipelines commonly utilize dataframe and array operations for tasks such as data preprocessing, analysis, and machine learning. The most popular tools for these tasks…

cs.LG2023

Create and Find Flatness: Building Flat Training Spaces in Advance for Continual Learning

Wenhang Shi, Yiren Chen, Zhe Zhao +3

Catastrophic forgetting remains a critical challenge in the field of continual learning, where neural networks struggle to retain prior knowledge while assimilating new information…

cs.DB20231 cited

ChatPipe: Orchestrating Data Preparation Program by Optimizing Human-ChatGPT Interactions

Sibei Chen, Hanbing Liu, Weiting Jin +5

Orchestrating a high-quality data preparation program is essential for successful machine learning (ML), but it is known to be time and effort consuming. Despite the impressive cap…