29 citations · 81 across the 15 of their papers we have counts for
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cs.IR2023
SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems
Beichuan Zhang, Chenggen Sun, Jianchao Tan +7
Increasing the size of embedding layers has shown to be effective in improving the performance of recommendation models, yet gradually causing their sizes to exceed terabytes in in…
cs.CV2023
Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks
Jue Chen, Huan Yuan, Jianchao Tan +3
Brain-inspired Spiking Neural Networks (SNNs) have the characteristics of event-driven and high energy-efficient, which are different from traditional Artificial Neural Networks (A…
cs.CV2023
PA&DA: Jointly Sampling PAth and DAta for Consistent NAS
Shun Lu, Yu Hu, Longxing Yang +4
Based on the weight-sharing mechanism, one-shot NAS methods train a supernet and then inherit the pre-trained weights to evaluate sub-models, largely reducing the search cost. Howe…