用变分推断加速贝叶斯神经网络的伪核心集训练，节省内存并提升性能。
Variational Bayesian Pseudo-Coreset
- 采用变分推断近似权重后验分布，降低计算开销。
- 在多个基准数据集上表现优于现有方法，且内存占用更少。
- 适合需要高效贝叶斯学习和不确定性估计的研究者。
深度学习的成功依赖于大规模数据集和长时间训练，带来显著计算挑战。为应对这些问题，伪核心集——一种可学习的小型数据集，能模拟完整数据集——被提出。然而，贝叶斯神经网络因参数空间维度高，在处理大规模数据时也面临计算难题。已有研究尝试通过少量伪核心集减少权重后验分布的计算负担，但存在训练期间内存效率低和结果不优的问题。为此，我们提出变分贝叶斯伪核心集（VBPC），利用变分推断高效逼近后验分布，显著降低内存使用和计算成本，同时在多个基准数据集上实现更优性能。
原文摘要 · Abstract (English)
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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