Data collection and quality challenges in deep learning: A data-centric ai perspective

SE Whang, Y Roh, H Song, JG Lee - The VLDB Journal, 2023 - Springer
Data-centric AI is at the center of a fundamental shift in software engineering where machine
learning becomes the new software, powered by big data and computing infrastructure …

Data collection and quality challenges for deep learning

SE Whang, JG Lee - Proceedings of the VLDB Endowment, 2020 - dl.acm.org
Software 2.0 refers to the fundamental shift in software engineering where using machine
learning becomes the new norm in software with the availability of big data and computing …

A process model for systematically setting up the data basis for data-driven projects in manufacturing

S Meier, S Klarmann, N Thielen, C Pfefferer… - Journal of Manufacturing …, 2023 - Elsevier
In the rapidly advancing fields of Artificial Intelligence (AI) and Big Data, creating a robust
and high-quality data foundation is a critical requirement for data-driven projects. However …

[HTML][HTML] Adaptive data quality scoring operations framework using drift-aware mechanism for industrial applications

F Bayram, BS Ahmed, E Hallin - Journal of Systems and Software, 2024 - Elsevier
Within data-driven artificial intelligence (AI) systems for industrial applications, ensuring the
reliability of the incoming data streams is an integral part of trustworthy decision-making. An …

Mlops spanning whole machine learning life cycle: A survey

F Zhengxin, Y Yi, Z **gyu, L Yue, M Yuechen… - ar** Imbalance into Balance: Active Robot Guidance of Human Teachers for Better Learning from Demonstrations
M Hou, K Hindriks, AE Eiben… - 2023 32nd IEEE …, 2023 - ieeexplore.ieee.org
Learning from Demonstrations (LfD) transfers skills from human teachers to robots.
However, data imbalance in demonstrations can bias policies towards majority situations …