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Found 9 papers, 4 papers with code
FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation
In particular, we formulate (1) prominent region of interest (ROI) features extraction, (2) brain networks generation, and (3) clinical predictions with GNNs, in an end-to-end trainable model under the guidance of particular prediction tasks.
Identifying Critical LMS Features for Predicting At-risk Students
Unsupervised learning was also used to group students into different clusters based on the similarities in their interaction/involvement with LMS.
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs.
Unrestricted Adversarial Attacks on ImageNet Competition
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input.
Generating Transferable Adversarial Patch by Simultaneously Optimizing its Position and Perturbations
In this paper, we propose a method to simultaneously optimize the position and perturbation to generate transferable adversarial patches, and thus obtain high attack success rates in the black-box setting.
Effective and Interpretable fMRI Analysis via Functional Brain Network Generation
Recent studies in neuroscience show great potential of functional brain networks constructed from fMRI data for popularity modeling and clinical predictions.
Improving Adversarial Transferability with Gradient Refining
To improve the transferability of adversarial examples for the black-box setting, several methods have been proposed, e. g., input diversity, translation-invariant attack, and momentum-based attack.
Meaningful Adversarial Stickers for Face Recognition in Physical World
Face recognition (FR) systems have been widely applied in safety-critical fields with the introduction of deep learning.
LOCUS: A Novel Decomposition Method for Brain Network Connectivity Matrices using Low-rank Structure with Uniform Sparsity
In this paper, we propose a novel blind source separation method with low-rank structure and uniform sparsity (LOCUS) as a fully data-driven decomposition method for network measures.
