Dynamic Post-Hoc Neural Ensemblers
Keywords: ensemble learning, neural networks, regularization, modality agnostic, dynamic ensembling
TL;DR: We introduce a neural network that learns to ensemble by assigning weights dynamically to base models.
Abstract: Ensemble methods are known for enhancing the accuracy and robustness of machine learning models by combining multiple base learners. However, standard approaches like greedy or random ensembles often fall short, as they assume a constant weight across samples for the ensemble members. This can limit expressiveness and hinder performance when aggregating the ensemble predictions. In this study, we explore employing neural networks as ensemble methods, emphasizing the significance of dynamic ensembling to leverage diverse model predictions adaptively. Motivated by the risk of learning low-diversity ensembles, we propose regularizing the model by randomly dropping base model predictions during the training. We demonstrate this approach lower bounds the diversity within the ensemble, reducing overfitting and improving generalization capabilities. Our experiments showcase that the dynamic neural ensemblers yield competitive results compared to strong baselines in computer vision, natural language processing, and tabular data.
Primary Area: other topics in machine learning (i.e., none of the above)
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Submission Number: 6439
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