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What are the challenges and limitations of using machine learning with small data? 


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Machine learning with small data poses several challenges and limitations. The limited availability of data makes it difficult to achieve reliable model generalization and transferability, leading to poor performance in real-world applications . Small data problems are compounded by issues such as data diversity, imputation, noise, imbalance, and high-dimensionality . Additionally, the small sample size hinders the ability to learn and generalize effectively, which is a key difference between human and artificial intelligence . The nature of experimental organic chemistry often restricts practitioners to small datasets, limiting the application of machine learning techniques . To address these challenges, various techniques have been proposed, including transfer learning, self-supervised learning, and generative models, which have shown promising potential in overcoming the limitations of small data . By adopting a holistic data-centric approach and leveraging statistical analysis, the value of small data can be maximized in chemistry research .

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The paper discusses the limitations of using small data in machine learning, including the impact of bias and variance on constructing reliable predictive models.
The paper discusses that the challenges of using machine learning with small data include the difficulty of discovering patterns and the impracticality of artificial intelligence models in limited data scenarios.
The paper discusses the challenges of using small data in deep learning applications, including poor model generalizability and transferability.
The paper discusses challenges such as data diversity, imputation, noise, imbalance, and high-dimensionality in using machine learning with small data.

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