A Compact Network Learning Model for Distribution Regression

Understand

Despite the superior performance of deep learning in many applications, challenges remain in the area of regression on function spaces.

• In particular, neural networks are unable to encode function inputs compactly as each node encodes just a real value.
• We propose a novel idea to address this shortcoming: to encode an entire function in a single network node.
• To that end, we design a compact network representation that encodes and propagates functions in single nodes for the distribution regression task.