A Framework for End-to-End Learning on Semantic Tree-Structured Data

13 Feb 2020William WoofKe Chen

While learning models are typically studied for inputs in the form of a fixed dimensional feature vector, real world data is rarely found in this form. In order to meet the basic requirement of traditional learning models, structural data generally have to be converted into fix-length vectors in a handcrafted manner, which is tedious and may even incur information loss... (read more)

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