4 citations · 12 across the 10 of their papers we have counts for
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cs.LG2022★ 4 cited
Enabling Deep Learning on Edge Devices through Filter Pruning and Knowledge Transfer
Kaiqi Zhao, Yitao Chen, Ming Zhao
Deep learning models have introduced various intelligent applications to edge devices, such as image classification, speech recognition, and augmented reality. There is an increasi…
cs.LG2022
Iterative Activation-based Structured Pruning
Kaiqi Zhao, Animesh Jain, Ming Zhao
Deploying complex deep learning models on edge devices is challenging because they have substantial compute and memory resource requirements, whereas edge devices' resource budget…
cs.CV2022★ 2 cited
Adaptive Activation-based Structured Pruning
Kaiqi Zhao, Animesh Jain, Ming Zhao
Pruning is a promising approach to compress complex deep learning models in order to deploy them on resource-constrained edge devices. However, many existing pruning solutions are…