Variational Bayesian Pseudo-Coreset

• URL: http://arxiv.org/abs/2502.21143v1
• Date: Fri, 28 Feb 2025 15:26:10 GMT
• Title: Variational Bayesian Pseudo-Coreset
• Authors: Hyungi Lee, Seungyoo Lee, Juho Lee,
• Abstract summary: Pseudo-coresets, small learnable datasets that mimic the entire data, have been proposed.<n>We propose Variational Bayesian Pseudo-Coreset (VBPC), a novel approach that utilizes variational inference to efficiently approximate the posterior distribution.
• Score: 14.400596021890863
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The success of deep learning requires large datasets and extensive training, which can create significant computational challenges. To address these challenges, pseudo-coresets, small learnable datasets that mimic the entire data, have been proposed. Bayesian Neural Networks, which offer predictive uncertainty and probabilistic interpretation for deep neural networks, also face issues with large-scale datasets due to their high-dimensional parameter space. Prior works on Bayesian Pseudo-Coresets (BPC) attempt to reduce the computational load for computing weight posterior distribution by a small number of pseudo-coresets but suffer from memory inefficiency during BPC training and sub-optimal results. To overcome these limitations, we propose Variational Bayesian Pseudo-Coreset (VBPC), a novel approach that utilizes variational inference to efficiently approximate the posterior distribution, reducing memory usage and computational costs while improving performance across benchmark datasets.

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