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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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Test-time local training of neural network for tabular data.

Myeonginn Kang¹, Seokho Kang²

¹Department of Industrial Engineering, Sungkyunkwan University, Jangan-gu, 16419, Suwon, Republic of Korea.

Scientific Reports

|December 9, 2025

Summary

This summary is machine-generated.

This study introduces a novel test-time local training method for neural networks on tabular data. It improves generalization by fine-tuning models with nearest neighbors for better local structure adaptation.

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Area of Science:

Machine Learning
Data Science
Artificial Intelligence

Background:

Neural networks are typically trained globally, optimizing parameters on entire datasets.
Global training can overlook local data structures, potentially harming generalization in sparse regions.
Existing test-time adaptation methods often focus on vision domains, not readily applying to tabular data.

Purpose of the Study:

To propose a test-time local training method specifically for tabular data.
To enhance neural network generalization by adapting to local data structures during inference.
To address limitations of global training in low-density data regions.

Main Methods:

A test-time local training approach is introduced for tabular data.
For each query instance, nearest neighbors are identified from the training dataset.
The globally trained neural network is localized via fine-tuning with these neighbors.

Main Results:

Experiments on tabular benchmark datasets (regression and classification) were conducted.
The proposed method demonstrated significant enhancement in neural network generalization ability.
Local adaptation improved performance, especially in regions with sparse data.

Conclusions:

The proposed test-time local training method effectively improves neural network generalization on tabular data.
Adapting models to local structures around query instances is crucial for performance.
This approach offers a viable solution for enhancing neural network robustness in diverse data distributions.