4 citations · 8 across the 5 of their papers we have counts for
3 papers
cs.LG2023★ 3 cited
Automatic Attention Pruning: Improving and Automating Model Pruning using Attentions
Kaiqi Zhao, Animesh Jain, Ming Zhao
Pruning is a promising approach to compress deep learning models in order to deploy them on resource-constrained edge devices. However, many existing pruning solutions are based on…
cs.LG2023★ 1 cited
A Contrastive Knowledge Transfer Framework for Model Compression and Transfer Learning
Kaiqi Zhao, Yitao Chen, Ming Zhao
Knowledge Transfer (KT) achieves competitive performance and is widely used for image classification tasks in model compression and transfer learning. Existing KT works transfer th…
cs.DC2023★ 2 cited
GPU-enabled Function-as-a-Service for Machine Learning Inference
Ming Zhao, Kritshekhar Jha, Sungho Hong
Function-as-a-Service (FaaS) is emerging as an important cloud computing service model as it can improve the scalability and usability of a wide range of applications, especially M…