Ensemble reservoir computing's prediction uncertainty serves as a data-driven indicator of local dynamical properties in spatiotemporal chaotic systems, matching known measures like Lyapunov spectra.
Ensemble learning: A survey.Wiley interdisciplinary reviews: data mining and knowledge discovery, 8(4):e1249
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PACE interleaves active generation of diverse learners with subsequent pruning to produce smaller ensembles that retain performance and offer faithfulness guarantees.
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Data-driven characterization of spatiotemporal chaos using ensemble reservoir computing
Ensemble reservoir computing's prediction uncertainty serves as a data-driven indicator of local dynamical properties in spatiotemporal chaotic systems, matching known measures like Lyapunov spectra.
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PACE: Prune-And-Compress Ensemble Models
PACE interleaves active generation of diverse learners with subsequent pruning to produce smaller ensembles that retain performance and offer faithfulness guarantees.