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Found 3 papers, 2 papers with code
Distilling Wikipedia mathematical knowledge into neural network models
Machine learning applications to symbolic mathematics are becoming increasingly popular, yet there lacks a centralized source of real-world symbolic expressions to be used as training data.
Cars Can't Fly up in the Sky: Improving Urban-Scene Segmentation via Height-driven Attention Networks
This paper exploits the intrinsic features of urban-scene images and proposes a general add-on module, called height-driven attention networks (HANet), for improving semantic segmentation for urban-scene images.
Ranked #17 on
Semantic Segmentation
on Cityscapes test
(using extra training data)
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence.
