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Found 6 papers, 2 papers with code
Upper Bound of Bayesian Generalization Error in Partial Concept Bottleneck Model (CBM): Partial CBM outperforms naive CBM
In this paper, we reveal the Bayesian generalization error in PCBM with a three-layered and linear architecture.
Bayesian Generalization Error in Linear Neural Networks with Concept Bottleneck Structure and Multitask Formulation
However, it has not yet been possible to understand the behavior of the generalization error in CBM since a neural network is a singular statistical model in general.
The Exact Asymptotic Form of Bayesian Generalization Error in Latent Dirichlet Allocation
The theoretical result shows that the Bayesian generalization error in LDA is expressed in terms of that in matrix factorization and a penalty from the simplex restriction of LDA's parameter region.
Variational Approximation Error in Bayesian Non-negative Matrix Factorization
However, the variational approximation error has not been clarified yet, because NMF is not statistically regular and the prior distribution used in variational Bayesian NMF (VBNMF) has zero or divergence points.
Asymptotic Bayesian Generalization Error in Latent Dirichlet Allocation and Stochastic Matrix Factorization
Latent Dirichlet allocation (LDA) is useful in document analysis, image processing, and many information systems; however, its generalization performance has been left unknown because it is a singular learning machine to which regular statistical theory can not be applied.
Upper Bound of Bayesian Generalization Error in Non-negative Matrix Factorization
Non-negative matrix factorization (NMF) is a new knowledge discovery method that is used for text mining, signal processing, bioinformatics, and consumer analysis.
