A structure-aware VAE generates realistic FC matrices for replay, combined with multi-level knowledge distillation and hierarchical contextual bandit sampling, to enable continual fMRI-based brain disorder diagnosis across sequentially arriving multi-site data without catastrophic forgetting.
Universal graph continual learning
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
G2LoRA proposes category-aware gradient projection and magnitude modulation within a unified graph-text alignment objective to mitigate interference and promote transfer in continual learning on text-attributed graphs.
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Continual Learning for fMRI-Based Brain Disorder Diagnosis via Functional Connectivity Matrices Generative Replay
A structure-aware VAE generates realistic FC matrices for replay, combined with multi-level knowledge distillation and hierarchical contextual bandit sampling, to enable continual fMRI-based brain disorder diagnosis across sequentially arriving multi-site data without catastrophic forgetting.
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G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs
G2LoRA proposes category-aware gradient projection and magnitude modulation within a unified graph-text alignment objective to mitigate interference and promote transfer in continual learning on text-attributed graphs.