307 citations · 693 across the 70 of their papers we have counts for
19 papers · 1 filter
Two For The Price Of One: Curvature-Aware Pruning Preserves Influence Functions Too
Mehdi Makni, Natalia Ponomareva, Rahul Mazumder
Post-training pruning enables efficient deployment, but conventional methods do not explicitly preserve internal-gradient alignment between sparse and dense models. We study novel…
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…
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…
MOONSHOT : A Framework for Multi-Objective Pruning of Vision and Large Language Models
Gabriel Afriat, Xiang Meng, Shibal Ibrahim +2
Weight pruning is a common technique for compressing large neural networks. We focus on the challenging post-training one-shot setting, where a pre-trained model is compressed with…
ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models
Xiang Meng, Kayhan Behdin, Haoyue Wang +1
The impressive performance of Large Language Models (LLMs) across various natural language processing tasks comes at the cost of vast computational resources and storage requiremen…
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning
Xiang Meng, Wenyu Chen, Riade Benbaki +1
The increasing computational demands of modern neural networks present deployment challenges on resource-constrained devices. Network pruning offers a solution to reduce model size…