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Variational Bayesian Pseudo-Coreset

arXiv 2502.21143 ↗First posted 28 Feb 2025cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 48 of its 77 references that are in the map. First posted on arXiv 28 Feb 2025.

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48 of 77 references.

References · 48

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36 more
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