375 citations · 433 across the 16 of their papers we have counts for
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cs.LG2024★ 1 cited
Towards Accurate and Efficient Sub-8-Bit Integer Training
Wenjin Guo, Donglai Liu, Weiying Xie +7
Neural network training is a memory- and compute-intensive task. Quantization, which enables low-bitwidth formats in training, can significantly mitigate the workload. To reduce qu…
cs.LG2024
HeteroTune: Efficient Federated Learning for Large Heterogeneous Models
Ruofan Jia, Weiying Xie, Jie Lei +3
While large pre-trained models have achieved impressive performance across AI tasks, their deployment in privacy-sensitive and distributed environments remains challenging. Federat…
cs.LG2023
Physics Inspired Criterion for Pruning-Quantization Joint Learning
Weiying Xie, Xiaoyi Fan, Xin Zhang +3
Pruning-quantization joint learning always facilitates the deployment of deep neural networks (DNNs) on resource-constrained edge devices. However, most existing methods do not joi…