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SVD & Low-Rank
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2026
06-26
EN
SigmaScale: Learning to Scale Weight Matrices for Better SVD-Based LLM Compression
06-26
中
SigmaScale 阅读笔记:通过学习缩放矩阵改进 SVD 大语言模型压缩
06-19
EN
LASER: How Throwing Away 99% of a Weight Matrix Can Make LLMs Smarter
06-19
中
LASER:丢掉 99% 的矩阵秩,LLM 推理准确率反而提高了 27%
06-12
EN
SliceGPT: Post-Training LLM Compression via Computational Invariance
06-12
中
SliceGPT 阅读笔记:用计算不变性删除 Transformer 的行与列
05-29
EN
IO-SVD: Input-Output Whitened SVD for Adaptive-Rank LLM Compression
05-29
中
IO-SVD:基于输入输出双侧白化的自适应秩LLM压缩方法
05-22
EN
DoRA: Weight-Decomposed Low-Rank Adaptation — Technical Review
05-22
中
DoRA:权重分解低秩自适应——用幅度与方向解耦提升 LoRA 学习能力 | 阅读笔记
05-15
EN
Zero Sum SVD: A Global, Loss-Aware Rank Budget for LLM Compression
05-15
中
Zero Sum SVD:用「损失零和」做全局奇异值预算分配的 LLM 压缩方法
05-08
EN
Swift-SVD: Activation-Aware Low-Rank Compression for LLM Weights and KV Cache
05-01
EN
Low-Rank Optimization Trajectories for LLM RLVR Acceleration: A Technical Review of NExt
04-10
EN
SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression — Deep Technical Review
04-10
中
SVD-LLM:面向大语言模型压缩的“截断感知”奇异值分解方法 — 深度阅读笔记
03-27
EN
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection — In-Depth Technical Review
03-23
EN
MiRA: A Subgoal-driven Framework for Improving Long-Horizon LLM Agents — Technical Review
03-13
EN
LoRA: Fine-Tuning Giant Models with Pocket Change — The Low-Rank Revolution