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Found 4 papers, 2 papers with code
Substructure Aware Graph Neural Networks
Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs struggle to break through the upper limit of the expressiveness of first-order Weisfeiler-Leman graph isomorphism test algorithm (1-WL) due to the consistency of the propagation paradigm of GNNs with the 1-WL. Based on the fact that it is easier to distinguish the original graph through subgraphs, we propose a novel framework neural network framework called Substructure Aware Graph Neural Networks (SAGNN) to address these issues.
Ranked #7 on
Graph Regression
on ZINC-500k
Modeling Household Online Shopping Demand in the U.S.: A Machine Learning Approach and Comparative Investigation between 2009 and 2017
Extensive post-modeling investigation is conducted in a comparative manner between 2009 and 2017, including quantifying the importance of each input variable in predicting online shopping demand, and characterizing value-dependent relationships between demand and the input variables.
Bio-inspired Structure Identification in Language Embeddings
Word embeddings are a popular way to improve downstream performances in contemporary language modeling.
MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object Segmentation
To further demonstrate the generalization ability of our spatiotemporal learning framework, we extend MATNet to another relevant task: dynamic visual attention prediction (DVAP).
Ranked #4 on
Video Polyp Segmentation
on SUN-SEG-Hard (Unseen)
