Despite their many desirable properties, Gaussian processes (GPs) are often compared unfavorably to deep neural networks (NNs) for lacking the ability to learn representations. Recent efforts to bridge the gap between GPs and deep NNs have yielded a new class of inter-domain variational GPs in which the inducing variables correspond to hidden units of a feedforward NN. In this work, we examine some practical issues associated with this approach and propose an extension that leverages the orthogonal decomposition of GPs to mitigate these limitations. In particular, we introduce spherical inter-domain features to construct more flexible data-dependent basis functions for both the principal and orthogonal components of the GP approximation and show that incorporating NN activation features under this framework not only alleviates these shortcomings but is more scalable than alternative strategies. Experiments on multiple benchmark datasets demonstrate the effectiveness of our approach.

本篇论文着重于解决高斯过程(GPs)对于深度神经网络(NNs)所缺乏的问题，通过介绍球形跨域特征，将处于NNs隐藏单元中的感应变量作为新型跨域变分GPs的载体，从而提高GP拟合的灵活性、数据适应性以及可伸缩性。实验结果表明，该方法的有效性得到了多个基准数据集的验证。

球面感应特征用于正交分解高斯过程