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cs.LG2026
ThinQuant: Scalable Rotation Learning for Weight and Activation Quantization of LLMs
Mehdi Makni, Ryan Lucas, Rahul Mazumder
Learned rotations play an important role in enabling low-bit weight and activation quantization of large language models by smoothing outliers in the activation distribution. State…
cs.LG2026
ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models
Ryan Lucas, Mehdi Makni, Xiang Meng +2
Quantization is an effective strategy to reduce the storage and computation footprint of large language models (LLMs). Post-training quantization (PTQ) is a leading approach for co…
cs.LG2024
Preserving Deep Representations In One-Shot Pruning: A Hessian-Free Second-Order Optimization Framework
Ryan Lucas, Rahul Mazumder
We present SNOWS, a one-shot post-training pruning framework aimed at reducing the cost of vision network inference without retraining. Current leading one-shot pruning methods min…