4 citations · 4 across the 5 of their papers we have counts for
6 papers
FastForward Pruning: Efficient LLM Pruning via Single-Step Reinforcement Learning
Xin Yuan, Siqi Li, Jiateng Wei +7
Pruning is an effective method for compressing Large Language Models, but finding an optimal, non-uniform layer-wise sparsity allocation remains a key challenge. While heuristic me…
Timestep-Aware Correction for Quantized Diffusion Models
Yuzhe Yao, Feng Tian, Jun Chen +4
Diffusion models have marked a significant breakthrough in the synthesis of semantically coherent images. However, their extensive noise estimation networks and the iterative gener…
AutoDFP: Automatic Data-Free Pruning via Channel Similarity Reconstruction
Siqi Li, Jun Chen, Jingyang Xiang +2
Structured pruning methods are developed to bridge the gap between the massive scale of neural networks and the limited hardware resources. Most current structured pruning methods…
M2-CLIP: A Multimodal, Multi-task Adapting Framework for Video Action Recognition
Mengmeng Wang, Jiazheng Xing, Boyuan Jiang +6
Recently, the rise of large-scale vision-language pretrained models like CLIP, coupled with the technology of Parameter-Efficient FineTuning (PEFT), has captured substantial attrac…
CR-SFP: Learning Consistent Representation for Soft Filter Pruning
Jingyang Xiang, Zhuangzhi Chen, Jianbiao Mei +3
Soft filter pruning~(SFP) has emerged as an effective pruning technique for allowing pruned filters to update and the opportunity for them to regrow to the network. However, this p…
SUBP: Soft Uniform Block Pruning for 1xN Sparse CNNs Multithreading Acceleration
Jingyang Xiang, Siqi Li, Jun Chen +4
The study of sparsity in Convolutional Neural Networks (CNNs) has become widespread to compress and accelerate models in environments with limited resources. By constraining N cons…