384 citations · 398 across the 3 of their papers we have counts for
4 papers
Learning Graph Augmentations to Learn Graph Representations
Kaveh Hassani, Amir Hosein Khasahmadi
Devising augmentations for graph contrastive learning is challenging due to their irregular structure, drastic distribution shifts, and nonequivalent feature spaces across datasets…
PointMask: Towards Interpretable and Bias-Resilient Point Cloud Processing
Saeid Asgari Taghanaki, Kaveh Hassani, Pradeep Kumar Jayaraman +2
Deep classifiers tend to associate a few discriminative input variables with their objective function, which in turn, may hurt their generalization capabilities. To address this, o…
Contrastive Multi-View Representation Learning on Graphs
Kaveh Hassani, Amir Hosein Khasahmadi
We introduce a self-supervised approach for learning node and graph level representations by contrasting structural views of graphs. We show that unlike visual representation learn…
Memory-Based Graph Networks
Amir Hosein Khasahmadi, Kaveh Hassani, Parsa Moradi +2
Graph neural networks (GNNs) are a class of deep models that operate on data with arbitrary topology represented as graphs. We introduce an efficient memory layer for GNNs that can…