将深度核学习转化为可高效计算的贝叶斯神经网络。
From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation
- 用加性结构与诱导先验近似降低高维核函数计算负担。
- 在回归与分类任务中性能超越现有深度核学习方法。
- 适合需要可解释性与高效推理的机器学习应用。
深度核学习（DKL）结合了深度学习与高斯过程（GPs）的优势，近年来备受关注。然而，当高斯过程层输入维度较高时，其计算成本显著增加。为此，本文提出深度加性核（DAK）模型，包含：i）最后一层GP的加性结构；ii）对每个GP单元的诱导先验近似。该设计自然导出一种末层贝叶斯神经网络（BNN）架构。所提方法兼具DKL的可解释性与BNN的计算效率。实验表明，该方法在回归和分类任务中均优于当前最先进的DKL方法。
原文摘要 · Abstract (English)
With the strengths of both deep learning and kernel methods like Gaussian Processes (GPs), Deep Kernel Learning (DKL) has gained considerable attention in recent years. From the computational perspective, however, DKL becomes challenging when the input dimension of the GP layer is high. To address this challenge, we propose the Deep Additive Kernel (DAK) model, which incorporates i) an additive structure for the last-layer GP; and ii) induced prior approximation for each GP unit. This naturally leads to a last-layer Bayesian neural network (BNN) architecture. The proposed method enjoys the interpretability of DKL as well as the computational advantages of BNN. Empirical results show that the proposed approach outperforms state-of-the-art DKL methods in both regression and classification tasks.
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