# Machine learning on small size samples: A synthetic knowledge synthesis

arXiv: https://arxiv.org/abs/2103.01002 · HTML: https://mlsanity.com/paper/2103.01002/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 91 references that are in the map.
- First posted on arXiv 1 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [Small Data, Big Decisions: Model Selection in the Small-Data Regime](https://mlsanity.com/paper/2009.12583/) (2020)
- [Exploring Transfer Learning for Low Resource Emotional TTS](https://mlsanity.com/paper/1901.04276/) (2019)
- [Small Sample Learning in Big Data Era](https://mlsanity.com/paper/1808.04572/) (2018)
- [Finding Covid-19 from Chest X-rays using Deep Learning on a Small Dataset](https://mlsanity.com/paper/2004.02060/) (2020)
- [Learning to Classify from Impure Samples with High-Dimensional Data](https://mlsanity.com/paper/1801.10158/) (2018)
- [A Benchmark Study of Machine Learning Models for Online Fake News Detection](https://mlsanity.com/paper/1905.04749/) (2019)
- [Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset](https://mlsanity.com/paper/1905.07203/) (2019)
- [Hi Sigma, do I have the Coronavirus?: Call for a New Artificial Intelligence Approach to Support Health Care Professionals Dealing With The COVID-19 Pandemic](https://mlsanity.com/paper/2004.06510/) (2020)
- [A comparison of Deep Learning performances with other machine learning algorithms on credit scoring unbalanced data](https://mlsanity.com/paper/1907.12363/) (2019)

## Built on by (most cited first, 11 of 11)

- [Deep Learning and LLM-based Methods Applied to Stellar Lightcurve Classification](https://mlsanity.com/paper/2404.10757/) (2024)
- [Rethinking the Evaluating Framework for Natural Language Understanding in AI Systems: Language Acquisition as a Core for Future Metrics](https://mlsanity.com/paper/2309.11981/) (2023)
- [Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation](https://mlsanity.com/paper/2406.08206/) (2024)
- [Uncertainty Quantification Using Ensemble Learning and Monte Carlo Sampling for Performance Prediction and Monitoring in Cell Culture Processes](https://mlsanity.com/paper/2409.02149/) (2024)
- [Lessons Learned from Deploying Adaptive Machine Learning Agents with Limited Data for Real-time Cell Culture Process Monitoring](https://mlsanity.com/paper/2509.02606/) (2025)
- [Protecting User Privacy in Online Settings via Supervised Learning](https://mlsanity.com/paper/2304.02870/) (2023)
- [New Product Development (NPD) through Social Media-based Analysis by Comparing Word2Vec and BERT Word Embeddings](https://mlsanity.com/paper/2304.08369/) (2023)
- [Gauge-optimal approximate learning for small data classification problems](https://mlsanity.com/paper/2310.19066/) (2023)
- [Small Data Explainer -- The impact of small data methods in everyday life](https://mlsanity.com/paper/2507.11773/) (2025)
- [Advantages and limitations in the use of transfer learning for individual treatment effects in causal machine learning](https://mlsanity.com/paper/2512.16489/) (2025)
- [Crossing the Validation Crisis: Cross-Validation Reduces Benchmarking Variance Surprisingly Well](https://mlsanity.com/paper/2606.12552/) (2026)

## 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.