384 citations · 441 across the 8 of their papers we have counts for
10 papers
Material Prediction for Design Automation Using Graph Representation Learning
Shijie Bian, Daniele Grandi, Kaveh Hassani +8
Successful material selection is critical in designing and manufacturing products for design automation. Designers leverage their knowledge and experience to create high-quality de…
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…
Cross-Domain Few-Shot Graph Classification
Kaveh Hassani
We study the problem of few-shot graph classification across domains with nonequivalent feature spaces by introducing three new cross-domain benchmarks constructed from publicly av…
Classifying Component Function in Product Assemblies with Graph Neural Networks
Vincenzo Ferrero, Kaveh Hassani, Daniele Grandi +1
Function is defined as the ensemble of tasks that enable the product to complete the designed purpose. Functional tools, such as functional modeling, offer decision guidance in the…
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…