most citedCatch-Up Distillation: You Only Need to Train Once for Accelerating Sampling

8 citations · 19 across the 7 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★ 1 cited

AIIR-MIX: Multi-Agent Reinforcement Learning Meets Attention Individual Intrinsic Reward Mixing Network

Wei Li, Weiyan Liu, Shitong Shao +1

Deducing the contribution of each agent and assigning the corresponding reward to them is a crucial problem in cooperative Multi-Agent Reinforcement Learning (MARL). Previous studi…