mlsanityData to 13 Oct 2026

Machine learning on small size samples: A synthetic knowledge synthesis

arXiv 2103.01002 ↗First posted 1 Mar 2021cs.LG cs.AIcited by 11

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Abstract (arXiv)

One of the increasingly important technologies dealing with the growing complexity of the digitalization of almost all human activities is Artificial intelligence, more precisely machine learning Despite the fact, that we live in a Big data world where almost everything is digitally stored, there are many real-world situations, where researchers are faced with small data samples. The present study aim is to answer the following research question namely What is the small data problem in machine learning and how it is solved?. Our bibliometric study showed a positive trend in the number of research publications concerning the use of small datasets and substantial growth of the research community dealing with the small dataset problem, indicating that the research field is moving toward higher maturity levels. Despite notable international cooperation, the regional concentration of research literature production in economically more developed countries was observed.

Key facts. Cited by 11 papers in the map (data through 13 Oct 2026). Builds on 9 of its 91 references that are in the map. First posted on arXiv 1 Mar 2021.

Builds on

9 of 91 references.

References · 9

  1. 2020Small Data, Big Decisions: Model Selection in the Small-Data Regimecited by 23
  2. 2019Exploring Transfer Learning for Low Resource Emotional TTScited by 21
  3. 2018Small Sample Learning in Big Data Eracited by 18
  4. 2020Finding Covid-19 from Chest X-rays using Deep Learning on a Small Datasetcited by 16
  5. 2018Learning to Classify from Impure Samples with High-Dimensional Datacited by 8
  6. 2019Transfer Learning based Detection of Diabetic Retinopathy from Small Datasetcited by 7
  7. 2020Hi Sigma, do I have the Coronavirus?: Call for a New Artificial Intelligence Approach to Support Health Care Professionals Dealing With The COVID-19 Pandemiccited by 7
  8. 2019A Benchmark Study of Machine Learning Models for Online Fake News Detectioncited by 6
  9. 2019A comparison of Deep Learning performances with other machine learning algorithms on credit scoring unbalanced datacited by 3

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 4; 2024: 3; 2025: 3; 2026: 1.

11 papers.

  1. 2024Deep Learning and LLM-based Methods Applied to Stellar Lightcurve Classificationcited by 3
  2. 2023Rethinking the Evaluating Framework for Natural Language Understanding in AI Systems: Language Acquisition as a Core for Future Metricscited by 2
  3. 2024Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimationcited by 2
  4. 2024Uncertainty Quantification Using Ensemble Learning and Monte Carlo Sampling for Performance Prediction and Monitoring in Cell Culture Processescited by 2
  5. 2025Lessons Learned from Deploying Adaptive Machine Learning Agents with Limited Data for Real-time Cell Culture Process Monitoringcited by 1
  6. 2023Protecting User Privacy in Online Settings via Supervised Learningcited by 0
  7. 2023New Product Development (NPD) through Social Media-based Analysis by Comparing Word2Vec and BERT Word Embeddingscited by 0
  8. 2023Gauge-optimal approximate learning for small data classification problemscited by 0
  9. 2025Small Data Explainer -- The impact of small data methods in everyday lifecited by 0
  10. 2025Advantages and limitations in the use of transfer learning for individual treatment effects in causal machine learningcited by 0
  11. 2026Crossing the Validation Crisis: Cross-Validation Reduces Benchmarking Variance Surprisingly Wellcited by 0