15 citations · 26 across the 7 of their papers we have counts for
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cs.LG2022★ 1 cited
Delving into Effective Gradient Matching for Dataset Condensation
Zixuan Jiang, Jiaqi Gu, Mingjie Liu +1
As deep learning models and datasets rapidly scale up, network training is extremely time-consuming and resource-costly. Instead of training on the entire dataset, learning with a…
cs.LG2021
Towards Memory-Efficient Neural Networks via Multi-Level in situ Generation
Jiaqi Gu, Hanqing Zhu, Chenghao Feng +4
Deep neural networks (DNN) have shown superior performance in a variety of tasks. As they rapidly evolve, their escalating computation and memory demands make it challenging to dep…
cs.LG2021★ 2 cited
Optimizer Fusion: Efficient Training with Better Locality and Parallelism
Zixuan Jiang, Jiaqi Gu, Mingjie Liu +2
Machine learning frameworks adopt iterative optimizers to train neural networks. Conventional eager execution separates the updating of trainable parameters from forward and backwa…