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A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Emerging Information Security and Applications
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IoT-Based Smartbots for Smart City Using MCC and Big Data
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Data distribution debugging in machine learning pipelines
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Algorithm selection for SMT
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Learning to Prompt for Vision-Language Models
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Data-Centric Artificial Intelligence
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AutoML technologies for the identification of sparse classification and outlier detection models
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Evaluation of data quality at the Hungarian National Cancer Registry, 2000–2019
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Data-centric Engineering: integrating simulation, machine learning and statistics. Challenges and opportunities
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Analyzing abnormal pattern of hotelling T2 control chart for compositional data using artificial neural networks
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Evaluation of deep unsupervised anomaly detection methods with a data-centric approach for on-line inspection
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National strategic artificial intelligence plans: A multi-dimensional analysis
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Inductive t-SNE via deep learning to visualize multi-label images
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Artificial Intelligence in Healthcare: Review, Ethics, Trust Challenges & Future Research Directions
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Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review
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An unsupervised feature selection approach for actionable warning identification
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Xel: A cloud-agnostic data platform for the design-driven building of high-availability data science services
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A systematic review of emerging information technologies for sustainable data-centric health-care
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A data-centric review of deep transfer learning with applications to text data
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A genetically-optimised artificial life algorithm for complexity-based synthetic dataset generation
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An IoT-based resource utilization framework using data fusion for smart environments
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Synthetic data as an enabler for machine learning applications in medicine
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Artificial Intelligence and COVID-19: A Systematic umbrella review and roads ahead
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The pipeline for the continuous development of artificial intelligence models—Current state of research and practice
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MC-GEN: Multi-level clustering for private synthetic data generation
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AI Applications through the Whole Life Cycle of Material Discovery
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Deep neural network to extract high-level features and labels in multi-label classification problems
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ydata-profiling: Accelerating data-centric AI with high-quality data
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LongReMix: Robust learning with high confidence samples in a noisy label environment
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Towards a sustainable future through data-centric solutions: potentials and challenges
inferredcites →https://doi.org/10.1016/j.procs.2022.12.366openalex-snapshot
Lessons learnt in industrial data platform integration
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Fusion of heterogeneous industrial data using polygon generation & deep learning
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DCEM: An R package for clustering big data via data-centric modification of Expectation Maximization
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Deploying artificial intelligence for climate change adaptation
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Foundation models for generalist medical artificial intelligence
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Moving towards vertically integrated artificial intelligence development
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Advances, challenges and opportunities in creating data for trustworthy AI
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Definition of a Practical Taxonomy for Referencing Data Quality Problems in Health Care Databases
inferredcites →https://doi.org/10.1108/fs-10-2021-0216openalex-snapshot
Artificial intelligence and big data: ontological and communicative perspectives in multi-sectoral scenarios of modern businesses
inferredcites →https://doi.org/10.1109/access.2021.3126656openalex-snapshot
When Two are Better Than One: Synthesizing Heavily Unbalanced Data
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Stop Oversampling for Class Imbalance Learning: A Review
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CNN Sensor Analytics With Hybrid-Float6 Quantization on Low-Power Embedded FPGAs
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Machine Learning Operations (MLOps): Overview, Definition, and Architecture
inferredcites →https://doi.org/10.1109/access.2024.3360705openalex-snapshot
Toward Green AI: A Methodological Survey of the Scientific Literature
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Opportunities and Challenges in Data-Centric AI
inferredcites →https://doi.org/10.1109/conecct55679.2022.9865824openalex-snapshot
Anomaly Detection in Time Series Data using Data-Centric AI
inferredcites →https://doi.org/10.1109/cvpr52729.2023.00352openalex-snapshot
A Data-Based Perspective on Transfer Learning
inferredcites →https://doi.org/10.1109/edge60047.2023.00052openalex-snapshot
Data-centric Edge-AI: A Symbolic Representation Use Case
inferredcites →https://doi.org/10.1109/iri49571.2020.00054openalex-snapshot
A New Emulation Platform for Real-time Machine Learning in Substance Use Data Streams
inferredcites →https://doi.org/10.1109/itt56123.2022.9863935openalex-snapshot
From Model-Centric to Data-Centric AI: A Paradigm Shift or Rather a Complementary Approach?
inferredcites →https://doi.org/10.1109/jproc.2020.3004555openalex-snapshot
A Comprehensive Survey on Transfer Learning
inferredcites →https://doi.org/10.1109/lcn53696.2022.9843735openalex-snapshot
MicroTL: Transfer Learning on Low-Power IoT Devices
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The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
inferredcites →https://doi.org/10.1109/mc.2022.3190785openalex-snapshot
Technomoral Affordances of Artificial Intelligence in Data-Driven Systems
inferredcites →https://doi.org/10.1109/mc.2023.3240450openalex-snapshot
Data-Centric Artificial Intelligence, Preprocessing, and the Quest for Transformative Artificial Intelligence Systems Development
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Strengthening and Democratizing Artificial Intelligence Research and Development
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Toward Building Edge Learning Pipelines
inferredcites →https://doi.org/10.1109/mitp.2023.3322410openalex-snapshot
Technical Analysis of Data-Centric and Model-Centric Artificial Intelligence
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A Review of the Privacy-Enhancing Technologies Software Market
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Andrew Ng, AI Minimalist: The Machine-Learning Pioneer Says Small is the New Big
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Potential Impact of Data-Centric AI on Society
inferredcites →https://doi.org/10.1109/sds57534.2023.00017openalex-snapshot
From Concept to Implementation: The Data-Centric Development Process for AI in Industry
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Data-Centric Green Artificial Intelligence: A Survey
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Navigating Data-Centric Artificial Intelligence With DC-Check: Advances, Challenges, and Opportunities
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NUS: Noisy-Sample-Removed Undersampling Scheme for Imbalanced Classification and Application to Credit Card Fraud Detection
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Mitigating Label Noise in GANs via Enhanced Spectral Normalization
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Defending Against Membership Inference Attacks With High Utility by GAN
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Deep Transferable Intelligence for Spatial Variability Characterization and Data-Efficient Learning in Biomechanical Measurement
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DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data
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Design of a Quantization-Based DNN Delta Compression Framework for Model Snapshots and Federated Learning
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On the Effectiveness of Transfer Learning for Code Search
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Challenges of Deep Learning in Medical Image Analysis—Improving Explainability and Trust
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Tasks and Visualizations Used for Data Profiling: A Survey and Interview Study
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Why emerging supply chain technologies initially disappoint: Blockchain, IoT, and AI
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Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI
inferredcites →https://doi.org/10.1145/3458817.3476176openalex-snapshot
Productivity, portability, performance
inferredcites →https://doi.org/10.1145/3534678.3542604openalex-snapshot
Advances in Exploratory Data Analysis, Visualisation and Quality for Data Centric AI Systems
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Towards Data-Efficient Continuous Learning for Edge Video Analytics via Smart Caching
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Data-centric AI: Techniques and Future Perspectives
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A Data-centric AI Framework for Automating Exploratory Data Analysis and Data Quality Tasks
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Bridging Domain Expertise and AI through Data Understanding
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Temporal Effects on Pre-trained Models for Language Processing Tasks
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A data-centric approach to anomaly detection in layer-based additive manufacturing
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Enabling data-centric AI through data quality management and data literacy
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DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation
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Steps Toward Robust Artificial Intelligence
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From Artificial Intelligence (AI) to Intelligence Augmentation (IA): Design Principles, Potential Risks, and Emerging Issues
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Data Stewardship Plan templates designed to support the FAIR principles
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An Open Medical Platform to Share Source Code and Various Pre-Trained Weights for Models to Use in Deep Learning Research
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Data-Centric and Model-Centric AI: Twin Drivers of Compact and Robust Industry 4.0 Solutions
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TSxtend: A Tool for Batch Analysis of Temporal Sensor Data
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TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs
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Introduction to Taking the Fear out of Data Analysis
inferredcites →https://doi.org/10.48550/arxiv.2202.08836openalex-snapshot
Data-SUITE: Data-centric identification of in-distribution incongruous examples
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BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing
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Image Quality Assessment: Integrating Model-Centric and Data-Centric Approaches
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KoopaML: A Graphical Platform for Building Machine Learning Pipelines Adapted to Health Professionals.
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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