Deep models trained in supervised mode have achieved remarkable success on a variety of tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a …
W Saeed, C Omlin - Knowledge-Based Systems, 2023 - Elsevier
The past decade has seen significant progress in artificial intelligence (AI), which has resulted in algorithms being adopted for resolving a variety of problems. However, this …
S Miao, M Liu, P Li - International Conference on Machine …, 2022 - proceedings.mlr.press
Interpretable graph learning is in need as many scientific applications depend on learning models to collect insights from graph-structured data. Previous works mostly focused on …
The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis,,,,,,,–. Deep learning approaches have aided in exploring …
We consider the problem of explaining the predictions of graph neural networks (GNNs), which otherwise are considered as black boxes. Existing methods invariably focus on …
Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features--rationale--which guides the model prediction. Unfortunately, the leading …
Graph representation learning has shown effectiveness when testing and training graph data come from the same distribution, but most existing approaches fail to generalize under …
This work studies the evaluation of explaining graph neural networks (GNNs), which is crucial to the credibility of post-hoc explainability in practical usage. Conventional evaluation …
Despite recent success in using the invariance principle for out-of-distribution (OOD) generalization on Euclidean data (eg, images), studies on graph data are still limited …