1 citations · 1 across the 5 of their papers we have counts for
5 papers
ASP: Automatic Selection of Proxy dataset for efficient AutoML
Peng Yao, Chao Liao, Jiyuan Jia +4
Deep neural networks have gained great success due to the increasing amounts of data, and diverse effective neural network designs. However, it also brings a heavy computing burden…
USDC: Unified Static and Dynamic Compression for Visual Transformer
Huan Yuan, Chao Liao, Jianchao Tan +5
Visual Transformers have achieved great success in almost all vision tasks, such as classification, detection, and so on. However, the model complexity and the inference speed of t…
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…
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…
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…