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Found 3 papers, 1 papers with code
Active Few-Shot Fine-Tuning
We study the active few-shot fine-tuning of large neural networks to downstream tasks.
Information-based Transductive Active Learning
We generalize active learning to address real-world settings where sampling is restricted to an accessible region of the domain, while prediction targets may lie outside this region.
Tuning Legged Locomotion Controllers via Safe Bayesian Optimization
This paper presents a data-driven strategy to streamline the deployment of model-based controllers in legged robotic hardware platforms.
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