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Scalable Graph Neural Networks for Heterogeneous Graphs
LingfanYu, JiajunShen, JinyangLi....
Published date-11/19/2020
Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data. Recent work has argued that GNNs primarily use the graph for feature smoothing, and …
Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT
RuifengYuan, ZiliWang, WenjieLi....
Published date-11/19/2020
NaturalLanguageUnderstanding
Most current extractive summarization models generate summaries by selecting salient sentences. However, one of the problems with sentence-level extractive summarization is that there exists a gap between the human-written gold …
Robustness to Missing Features using Hierarchical Clustering with Split Neural Networks
RishabKhincha, UtkarshSarawgi, WazeerZulfikar....
Published date-11/19/2020
Clustering, Imputation
The problem of missing data has been persistent for a long time and poses a major obstacle in machine learning and statistical data analysis. Past works in this field have …
Node Similarity Preserving Graph Convolutional Networks
WeiJin, TylerDerr, YiqiWang....
Published date-11/19/2020
GraphRepresentationLearning, RepresentationLearning, Self-SupervisedLearning
Graph Neural Networks (GNNs) have achieved tremendous success in various real-world applications due to their strong ability in graph representation learning. GNNs explore the graph structure and node features by …
An Integrated Approach for Improving Brand Consistency of Web Content: Modeling, Analysis and Recommendation
SoumyadeepRoy, ShamikSural, NiyatiChhaya....
Published date-11/19/2020
A consumer-dependent (business-to-consumer) organization tends to present itself as possessing a set of human qualities, which is termed as the brand personality of the company. The perception is impressed upon …
FROST: Faster and more Robust One-shot Semi-supervised Training
HelenaE.Liu, LeslieN.Smith....
Published date-11/18/2020
Recent advances in one-shot semi-supervised learning have lowered the barrier for deep learning of new applications. However, the state-of-the-art for semi-supervised learning is slow to train and the performance is …