2 citations · 7 across the 30 of their papers we have counts for
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cs.LG2026
From Weights to Features: SAE-Guided Activation Regularization for LLM Continual Learning
Evan Ning, Wei Xue, Dong Lou +1
Weight-space regularization methods such as Elastic Weight Consolidation (EWC) are the standard approach to catastrophic forgetting in continual learning. However, those methods te…
cs.LG2025★ 1 cited
Delta Decompression for MoE-based LLMs Compression
Hao Gu, Wei Li, Lujun Li +5
Mixture-of-Experts (MoE) architectures in large language models (LLMs) achieve exceptional performance, but face prohibitive storage and memory requirements. To address these chall…
cs.LG2024★ 2 cited
NoRA: Nested Low-Rank Adaptation for Efficient Fine-Tuning Large Models
Cheng Lin, Lujun Li, Dezhi Li +3
In this paper, we introduce Nested Low-Rank Adaptation (NoRA), a novel approach to parameter-efficient fine-tuning that extends the capabilities of Low-Rank Adaptation (LoRA) techn…