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Experimental results suggest that the parameters of trained autoencoders can be viewed as sample representations, allowing for a high degree of accuracy in distinguishing between models trained on different data subsets, without resorting to complex vector generation algorithms or using the original samples.
An architecture search framework that makes the alignment between experts and the structure of the data an explicit search variable and ensures that the assignment of data clusters to experts is optimised jointly with the per-expert architectures is proposed.
A dual-objective surrogate-guided ensemble search is introduced: candidate architectures are represented as directed acyclic graphs, and two surrogate models are trained independently to estimate predictive accuracy and diversity potential and guide an NES framework that efficiently identifies architectures that are bo...
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