7 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…
OvSW: Overcoming Silent Weights for Accurate Binary Neural Networks
Jingyang Xiang, Zuohui Chen, Siqi Li +2
Binary Neural Networks~(BNNs) have been proven to be highly effective for deploying deep neural networks on mobile and embedded platforms. Most existing works focus on minimizing q…
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…
Learnable Chamfer Distance for Point Cloud Reconstruction
Tianxin Huang, Qingyao Liu, Xiangrui Zhao +2
As point clouds are 3D signals with permutation invariance, most existing works train their reconstruction networks by measuring shape differences with the average point-to-point d…
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…
MaxQ: Multi-Axis Query for N:M Sparsity Network
Jingyang Xiang, Siqi Li, Junhao Chen +4
N:M sparsity has received increasing attention due to its remarkable performance and latency trade-off compared with structured and unstructured sparsity. However, existing N:M spa…