mlsanityData to 7 Oct 2026

Machine learning and deep learning

arXiv 2104.05314 ↗First posted 12 Apr 2021cs.AIcited by 26

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Abstract (arXiv)

Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 10 of its 63 references that are in the map. First posted on arXiv 12 Apr 2021.

Builds on

10 of 63 references.

References · 10

  1. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  2. 2014Deep Learning in Neural Networks: An Overviewcited by 765
  3. 2019A Survey of Deep Learning Techniques for Autonomous Drivingcited by 401
  4. 2017Recent Trends in Deep Learning Based Natural Language Processingcited by 262
  5. 2018Market Making via Reinforcement Learningcited by 45
  6. 2020Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Modelscited by 43
  7. 2021Representation Learning for Natural Language Processingcited by 5
  8. 2020Augmenting Organizational Decision-Making with Deep Learning Algorithms: Principles, Promises, and Challengescited by 4
  9. 2018Evaluating the Utility of Hand-crafted Features in Sequence Labellingcited by 3
  10. 2020Gimme That Model!: A Trusted ML Model Trading Protocolcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 7; 2023: 6; 2024: 7; 2025: 5; 2026: 1.

26 papers.

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  1. 2023Generative AIcited by 119
  2. 2022Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainabilitycited by 21
  3. 2025A Survey of Zero-Knowledge Proof Based Verifiable Machine Learningcited by 21
  4. 2022FedStack: Personalized activity monitoring using stacked federated learningcited by 15
  5. 2024A review of graph neural network applications in mechanics-related domainscited by 14
  6. 2023A Comprehensive Review of Community Detection in Graphscited by 12
  7. 2024Rethinking model prototyping through the MedMNIST+ dataset collectioncited by 9
  8. 2023Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directionscited by 8
  9. 2024Ethio-Fake: Cutting-Edge Approaches to Combat Fake News in Under-Resourced Languages Using Explainable AIcited by 8
  10. 2022A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacousticcited by 7
  11. 2025Evaluation of LLMs for mathematical problem solvingcited by 7
  12. 2022GlacierNet2: A Hybrid Multi-Model Learning Architecture for Alpine Glacier Mappingcited by 6
14 more
  1. 2022Breast Cancer Classification using Deep Learned Features Boosted with Handcrafted Featurescited by 5
  2. 2024Machine learning in business process management: A systematic literature reviewcited by 5
  3. 2024A Comprehensive Survey on Machine Learning Driven Material Defect Detectioncited by 5
  4. 2024Machine Learning on Dynamic Graphs: A Survey on Applicationscited by 4
  5. 2022Survey and Systematization of 3D Object Detection Models and Methodscited by 3
  6. 2025Learning-based estimation of cattle weight gain and its influencing factorscited by 3
  7. 2023The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligencecited by 2
  8. 2023AI in Supply Chain Risk Assessment: A Systematic Literature Review and Bibliometric Analysiscited by 2
  9. 2025BrainIB++: Leveraging Graph Neural Networks and Information Bottleneck for Functional Brain Biomarkers in Schizophreniacited by 2
  10. 2022What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Reviewcited by 1
  11. 2024A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsiscited by 1
  12. 2025Neural Architecture Search for Quantum Autoencoderscited by 1
  13. 2023Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023cited by 0
  14. 2026Advanced Machine Learning and Deep Learning Techniques for Enhanced Cattle Identification and Detection: A Comprehensive Reviewcited by 0