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2 results for “meta-validation”

Optimizing Prediction Intervals by Tuning Random Forest via Meta-Validationcs.LG

This paper optimizes prediction intervals by tuning Random Forest using meta-validation.

problem Optimizing prediction intervals constructed by Random Forest.

method Combines exhaustive search with meta-validation techniques to tune Random Forest parameters.

result The 75/25 holdout meta-validation technique is always beneficial for tuning Random Forest.

Random Forest prediction intervals meta-validation tuning exhaustive search

2018-01-22abs ↗pdf ↗

Improving Meta-Learning Generalization with Activation-Based Early-Stoppingcs.LG

Early stopping in meta-learning improved by analyzing neural activation patterns.

problem Early stopping in few-shot learning is challenging due to distributional shifts between meta-validation and meta-test sets.

method Activation-Based Early-stopping (ABE) analyzes hidden layer activations from unlabelled support examples to detect when target generalization diverges from source data.

result Simple activation statistics can effectively estimate target generalization, improving few-shot transfer learning across various algorithms and datasets.

meta-learning few-shot learning early-stopping neural activations distributional shift

2022-08-03abs ↗pdf ↗