sep-CMA-ES outperforms Adam on a combined aesthetic-plus-alignment objective when optimizing prompt embeddings for Stable Diffusion XL Turbo across 36 Parti Prompts and three weight settings.
Completely derandomized self-adaptation in evolution strategies
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
RCRC uses untrained random CNNs and reservoir computing plus evolution strategies to reach claimed state-of-the-art scores in reinforcement learning tasks while avoiding data storage and heavy training.
citing papers explorer
-
Evolutionary Optimization Trumps Adam Optimization on Embedding Space Exploration
sep-CMA-ES outperforms Adam on a combined aesthetic-plus-alignment objective when optimizing prompt embeddings for Stable Diffusion XL Turbo across 36 Parti Prompts and three weight settings.
-
Convolutional Reservoir Computing for World Models
RCRC uses untrained random CNNs and reservoir computing plus evolution strategies to reach claimed state-of-the-art scores in reinforcement learning tasks while avoiding data storage and heavy training.