Deep learning provides an excellent avenue for optimizing diagnosis and
...
Successful material selection is critical in designing and manufacturing...
A wide range of models have been proposed for Graph Generative Models,
n...
Devising augmentations for graph contrastive learning is challenging due...
We study the problem of few-shot graph classification across domains wit...
Deep classifiers tend to associate a few discriminative input variables ...
We introduce a self-supervised approach for learning node and graph leve...
Graph neural networks (GNNs) are a class of deep models that operate on ...
We introduce a relational graph neural network with bi-directional atten...
We introduce an unsupervised multi-task model to jointly learn point and...
In this paper, a novel and generic multi-objective design paradigm is
pr...
A natural language interface exploits the conceptual simplicity and
natu...