Bayesian Optimization in Action

13 Combining Gaussian processes with neural networks

This chapter covers

• The difficulty of processing complex, structured data with common covariance functions
• Using neural networks to handle complex, structured data
• Combining neural networks with GPs

In chapter 2, we learned that the mean and covariance functions of a Gaussian process (GP) act as prior information that we’d like to incorporate into the model when making predictions. For this reason, the choice for these functions greatly affects how the trained GP behaves. Consequently, if the mean and covariance functions are misspecified or inappropriate for the task at hand, the resulting predictions won’t be useful.

As an example, remember that a covariance function, or kernel, expresses ...