Learning Arithmetic Circuits

Understand

Graphical models are usually learned without regard to the cost of doing inference with them.

• As a result, even if a good model is learned, it may perform poorly at prediction, because it requires approximate inference.
• We propose an alternative: learning models with a score function that directly penalizes the cost of inference.
• Specifically, we learn arithmetic circuits with a penalty on the number of edges in the circuit (in which the cost of inference is linear).