Synthetic-to-Real Data Ratios Governing Convergence and Accuracy in Low-Resource Tabular Foundation Models

Abstract

Abstract: Abstract Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase effi

Research Question

What is the impact of varying the ratio of SCM-generated synthetic samples to real data on the convergence speed and final accuracy of fine-tuned tabular foundation models in low-resource settings?

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