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cs.LG2026
Representation Finetuning for Continual Learning
Haihua Luo, Xuming Ran, Tommi Kärkkäinen +4
The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams. While pre-trained models have shown powerful performance in co…
cs.LG2025
Distillation-Guided Structural Transfer for Continual Learning Beyond Sparse Distributed Memory
Huiyan Xue, Xuming Ran, Yaxin Li +4
Sparse neural systems are gaining traction for efficient continual learning due to their modularity and low interference. Architectures such as Sparse Distributed Memory Multi-Laye…