We investigate the capabilities and limitations of Gaussian process models by
jointly exploring three complementary directions: (i) scalable and
statistically efficient inference; (ii) flexible kernels; and (iii) objective
functions for hyperparameter learning alternative to the marginal likelihood.
Our approach outperforms all previously reported GP methods on the standard
MNIST dataset; performs comparatively to previous kernel-based methods using
the RECTANGLES-IMAGE dataset; and breaks the 1% error-rate barrier in GP models
using the MNIST8M dataset, showing along the way the scalability of our method
at unprecedented scale for GP models (8 million observations) in classification
problems. Overall, our approach represents a significant breakthrough in kernel
methods and GP models, bridging the gap between deep learning approaches and
kernel machines.

该论文研究了高斯过程模型的能力和局限性，提出了一种具有可扩展性和统计效率的推理方法，探索了灵活的内核和用于学习超参数的客观函数。通过实验结果表明，这种方法在标准 MNIST 数据集上的性能优于所有之前报告的 GP 方法，并在 MNIST8M 数据集上突破了 1％的误差率，同时展示了它在分类问题中的可扩展性。总的来说，该方法在内核方法和 GP 模型方面取得了重大突破，缩小了深度学习方法和内核机器之间的差距。