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This paper proposes a Sparse-Activation-ReLU (SAR) layer for low-latency, energy-efficient virtual sensing, achieving significant improvements in latency-error-energy metrics and reducing errors through synthetic knowledge distillation.
This paper proposes a continual learning method for LLMs that uses pretrained sparse autoencoders (SAEs) to regularize in activation space instead of weight space, achieving better memory efficiency and stronger performance on benchmarks while avoiding catastrophic forgetting without storing previous data.