Autoregressive Conditional Neural Processes

Understand

Conditional neural processes (CNPs; Garnelo et al., 2018a) are attractive meta-learning models which produce well-calibrated predictions and are trainable via a simple maximum likelihood procedure.

• Although CNPs have many advantages, they are unable to model dependencies in their predictions.
• Various works propose solutions to this, but these come at the cost of either requiring approximate inference or being limited to Gaussian predictions.
• In this work, we instead propose to change how CNPs are deployed at test time, without any modifications to the model or training procedure.