REMIX uses Laplace kernel parameterization to enable scalable full-covariance modeling in model inversion, improving synthetic sample quality and performance in data-free continual learning.
A continual learning survey: Defying forgetting in classification tasks.IEEE transactions on pattern analysis and machine intelligence, 44(7):3366– 3385
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FLAME is an MoE architecture using modality-specific routers and low-rank compression of expert knowledge to support efficient continual multimodal multi-task learning while reducing catastrophic forgetting.
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Stop Marginalizing My Dreams: Model Inversion via Laplace Kernel for Continual Learning
REMIX uses Laplace kernel parameterization to enable scalable full-covariance modeling in model inversion, improving synthetic sample quality and performance in data-free continual learning.
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FLAME: Adaptive Mixture-of-Experts for Continual Multimodal Multi-Task Learning
FLAME is an MoE architecture using modality-specific routers and low-rank compression of expert knowledge to support efficient continual multimodal multi-task learning while reducing catastrophic forgetting.