CoMetaPNS combines meta-learned neural surrogates with a continual Bayesian Gaussian Mixture Model to adapt cardiac electrophysiology simulations to new data while avoiding catastrophic forgetting.
Dynamical variational autoencoders: A comprehensive review
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
FEAT mitigates representation collapse and prediction bias in federated continual learning by aligning feature angular similarities to shared Equiangular Tight Frame prototypes and removing task-irrelevant directional components from embeddings.
Proposes cross-attention audio-video fusion and VE-MD latent-space models for group emotion recognition that avoid individual cues and report competitive performance via ablation studies on synthetic and real data.
citing papers explorer
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CoMetaPNS: Continually Meta-learning Personalized Neural Surrogates for Cardiac Electrophysiology Simulations
CoMetaPNS combines meta-learned neural surrogates with a continual Bayesian Gaussian Mixture Model to adapt cardiac electrophysiology simulations to new data while avoiding catastrophic forgetting.
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From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity
FEAT mitigates representation collapse and prediction bias in federated continual learning by aligning feature angular similarities to shared Equiangular Tight Frame prototypes and removing task-irrelevant directional components from embeddings.
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Multimodal Group Emotion Recognition In-the-Wild Towards a Privacy-Safe Non-Individual Approach
Proposes cross-attention audio-video fusion and VE-MD latent-space models for group emotion recognition that avoid individual cues and report competitive performance via ablation studies on synthetic and real data.