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A compact network learning model for distribution regression
Ist Teil von
Neural networks, 2019-02, Vol.110, p.199-212
Ort / Verlag
United States: Elsevier Ltd
Erscheinungsjahr
2019
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
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. Our proposed distribution regression network (DRN) achieves higher prediction accuracies while using fewer parameters than traditional neural networks.