Data collection and quality challenges in deep learning: A data-centric ai perspective
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 …
learning becomes the new software, powered by big data and computing infrastructure …
The effects of data quality on machine learning performance
L Budach, M Feuerpfeil, N Ihde, A Nathansen… - arXiv preprint arXiv …, 2022 - arxiv.org
Modern artificial intelligence (AI) applications require large quantities of training and test
data. This need creates critical challenges not only concerning the availability of such data …
data. This need creates critical challenges not only concerning the availability of such data …
[HTML][HTML] Automated data processing and feature engineering for deep learning and big data applications: a survey
A Mumuni, F Mumuni - Journal of Information and Intelligence, 2024 - Elsevier
Modern approach to artificial intelligence (AI) aims to design algorithms that learn directly
from data. This approach has achieved impressive results and has contributed significantly …
from data. This approach has achieved impressive results and has contributed significantly …
Opportunities and Challenges in Data-Centric AI
Artificial intelligence (AI) systems are trained to solve complex problems and learn to
perform specific tasks by using large volumes of data, such as prediction, classification …
perform specific tasks by using large volumes of data, such as prediction, classification …
Selective data acquisition in the wild for model charging
The lack of sufficient labeled data is a key bottleneck for practitioners in many real-world
supervised machine learning (ML) tasks. In this paper, we study a new problem, namely …
supervised machine learning (ML) tasks. In this paper, we study a new problem, namely …
Construction of knowledge graphs: State and challenges
With knowledge graphs (KGs) at the center of numerous applications such as recommender
systems and question answering, the need for generalized pipelines to construct and …
systems and question answering, the need for generalized pipelines to construct and …
Goodcore: Data-effective and data-efficient machine learning through coreset selection over incomplete data
Given a dataset with incomplete data (eg, missing values), training a machine learning
model over the incomplete data requires two steps. First, it requires a data-effective step that …
model over the incomplete data requires two steps. First, it requires a data-effective step that …
Automated data cleaning can hurt fairness in machine learning-based decision making
In this paper, we interrogate whether data quality issues track demographic group
membership (based on sex, race and age) and whether automated data cleaning—of the …
membership (based on sex, race and age) and whether automated data cleaning—of the …
Data Cleaning and AutoML: Would an optimizer choose to clean?
Data cleaning is widely acknowledged as an important yet tedious task when dealing with
large amounts of data. Thus, there is always a cost-benefit trade-off to consider. In particular …
large amounts of data. Thus, there is always a cost-benefit trade-off to consider. In particular …
Data-centric machine learning for geospatial remote sensing data
Recent developments and research in modern machine learning have led to substantial
improvements in the geospatial field. Although numerous deep learning models have been …
improvements in the geospatial field. Although numerous deep learning models have been …