Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
Deep learning models often exhibit sensitivity to training data, leading to suboptimal performance. Traditional regularization techniques, while effective, can be inflexible and require extensive hyperparameter tuning. This paper introduces an adaptive topology for deep learning model regularization that dynamically adjusts the connectivity patterns within a neural network based on the characteristics of the training data. We propose a reinforcement learning-based algorithm to automatically optimize the topology, resulting in improved generalization and robustness. The core mechanism leverages the concept of a dynamically evolving network structure to mitigate the effects of data heterogeneity. The proposed approach offers a novel and potentially transformative solution for enhancing deep learning model performance.
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A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
