Active Learning
Cross-source consensus on Active Learning from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Where it comes from
Highlighted claims
- The framework used active learning and multi-objective Bayesian optimization to choose informative experiments sequentially. — Accelerating battery research with an AI interface between FINALES and Kadi4Mat
- The KadiAIgent plugin implemented Bayesian optimization and communicated with a Bayesian inference service. — Accelerating battery research with an AI interface between FINALES and Kadi4Mat
- The active-learning method used independent Gaussian process surrogate models for each objective. — Accelerating battery research with an AI interface between FINALES and Kadi4Mat
- Batches 11 through 17 were generated by the active-learning model after manually defined initial batches. — Accelerating battery research with an AI interface between FINALES and Kadi4Mat
- The acquisition strategy used q-noisy Expected Hypervolume Improvement to handle noisy measurements and batch optimization. — Accelerating battery research with an AI interface between FINALES and Kadi4Mat