1 citations · 2 across the 4 of their papers we have counts for
4 papers
TT-MPD: Test Time Model Pruning and Distillation
Haihang Wu, Wei Wang, Tamasha Malepathirana +3
Pruning can be an effective method of compressing large pre-trained models for inference speed acceleration. Previous pruning approaches rely on access to the original training dat…
LLM-BIP: Structured Pruning for Large Language Models with Block-Wise Forward Importance Propagation
Haihang Wu
Large language models (LLMs) have demonstrated remarkable performance across various language tasks, but their widespread deployment is impeded by their large size and high computa…
When To Grow? A Fitting Risk-Aware Policy for Layer Growing in Deep Neural Networks
Haihang Wu, Wei Wang, Tamasha Malepathirana +3
Neural growth is the process of growing a small neural network to a large network and has been utilized to accelerate the training of deep neural networks. One crucial aspect of ne…
Hybrid Pooling and Convolutional Network for Improving Accuracy and Training Convergence Speed in Object Detection
Shiwen Zhao, Wei Wang, Junhui Hou +1
This paper introduces HPC-Net, a high-precision and rapidly convergent object detection network.