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06/27/2023
Adversarial Training for Graph Neural Networks
Despite its success in the image domain, adversarial training does not (...
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05/01/2023
Revisiting Robustness in Graph Machine Learning
Many works show that node-level predictions of Graph Neural Networks (GN...
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01/31/2023
Transformers Meet Directed Graphs
Transformers were originally proposed as a sequence-to-sequence model fo...
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01/31/2023
Are Defenses for Graph Neural Networks Robust?
A cursory reading of the literature suggests that we have made a lot of ...
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01/05/2023
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks
Randomized smoothing is one of the most promising frameworks for certify...
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07/09/2022
On the Robustness and Anomaly Detection of Sparse Neural Networks
The robustness and anomaly detection capability of neural networks are c...
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06/21/2022
Winning the Lottery Ahead of Time: Efficient Early Network Pruning
Pruning, the task of sparsifying deep neural networks, received increasi...
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10/26/2021
Robustness of Graph Neural Networks at Scale
Graph Neural Networks (GNNs) are increasingly important given their popu...
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10/26/2021
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
The interdependence between nodes in graphs is key to improve class pred...
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10/21/2021
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness
End-to-end (geometric) deep learning has seen first successes in approxi...
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05/10/2021
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions
Uncertainty awareness is crucial to develop reliable machine learning mo...
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10/29/2020
