Likelihood-guided Regularization in Attention Based Models

• URL: http://arxiv.org/abs/2511.13221v1
• Date: Mon, 17 Nov 2025 10:38:09 GMT
• Title: Likelihood-guided Regularization in Attention Based Models
• Authors: Mohamed Salem, Inyoung Kim,
• Abstract summary: We propose a likelihood-guided variational Ising-based regularization framework for Vision Transformers (ViTs)<n>We show that the Ising regularizer leads to better-calibrated probability estimates and structured feature selection through uncertainty-aware attention mechanisms.
• Score: 1.561268797057701
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The transformer architecture has demonstrated strong performance in classification tasks involving structured and high-dimensional data. However, its success often hinges on large- scale training data and careful regularization to prevent overfitting. In this paper, we intro- duce a novel likelihood-guided variational Ising-based regularization framework for Vision Transformers (ViTs), which simultaneously enhances model generalization and dynamically prunes redundant parameters. The proposed variational Ising-based regularization approach leverages Bayesian sparsification techniques to impose structured sparsity on model weights, allowing for adaptive architecture search during training. Unlike traditional dropout-based methods, which enforce fixed sparsity patterns, the variational Ising-based regularization method learns task-adaptive regularization, improving both efficiency and interpretability. We evaluate our approach on benchmark vision datasets, including MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100, demonstrating improved generalization under sparse, complex data and allowing for principled uncertainty quantification on both weights and selection parameters. Additionally, we show that the Ising regularizer leads to better-calibrated probability estimates and structured feature selection through uncertainty-aware attention mechanisms. Our results highlight the effectiveness of structured Bayesian sparsification in enhancing transformer-based architectures, offering a principled alternative to standard regularization techniques.

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