79 citations · 140 across the 12 of their papers we have counts for
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cs.LG2023★ 8 cited
Catch-Up Distillation: You Only Need to Train Once for Accelerating Sampling
Shitong Shao, Xu Dai, Lujun Li +3
Diffusion Probability Models (DPMs) have made impressive advancements in various machine learning domains. However, achieving high-quality synthetic samples typically involves perf…
cs.LG2023★ 9 cited
GP-NAS-ensemble: a model for NAS Performance Prediction
Kunlong Chen, Liu Yang, Yitian Chen +3
It is of great significance to estimate the performance of a given model architecture without training in the application of Neural Architecture Search (NAS) as it may take a lot o…