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Found 11 papers, 7 papers with code
Attacks on Node Attributes in Graph Neural Networks
Graphs are commonly used to model complex networks prevalent in modern social media and literacy applications.
A Partially Supervised Reinforcement Learning Framework for Visual Active Search
Visual active search (VAS) has been proposed as a modeling framework in which visual cues are used to guide exploration, with the goal of identifying regions of interest in a large geospatial area.
A Visual Active Search Framework for Geospatial Exploration
Many problems can be viewed as forms of geospatial search aided by aerial imagery, with examples ranging from detecting poaching activity to human trafficking.
Reward Delay Attacks on Deep Reinforcement Learning
We find that this mitigation remains insufficient to ensure robustness to attacks that delay, but preserve the order, of rewards.
How Powerful are K-hop Message Passing Graph Neural Networks
Recently, researchers extended 1-hop message passing to K-hop message passing by aggregating information from K-hop neighbors of nodes simultaneously.
Adversarial Robustness without Adversarial Training: A Teacher-Guided Curriculum Learning Approach
Current SOTA adversarially robust models are mostly based on adversarial training (AT) and differ only by some regularizers either at inner maximization or outer minimization steps.
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning Approach
Current SOTA adversarially robust models are mostly based on adversarial training (AT) and differ only by some regularizers either at inner maximization or outer minimization steps.
A Framework for Learning Ante-hoc Explainable Models via Concepts
To the best of our knowledge, we are the first ante-hoc explanation generation method to show results with a large-scale dataset such as ImageNet.
Enhanced Regularizers for Attributional Robustness
Deep neural networks are the default choice of learning models for computer vision tasks.
Enforcing Linearity in DNN succours Robustness and Adversarial Image Generation
Recent studies on the adversarial vulnerability of neural networks have shown that models trained with the objective of minimizing an upper bound on the worst-case loss over all possible adversarial perturbations improve robustness against adversarial attacks.
ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness
Exploring adversarial attack vectors and studying their effects on machine learning algorithms has been of interest to researchers.
