19 citations · 33 across the 6 of their papers we have counts for
6 papers
Upper Bound of Bayesian Generalization Error in Partial Concept Bottleneck Model (CBM): Partial CBM outperforms naive CBM
Naoki Hayashi, Yoshihide Sawada
Concept Bottleneck Model (CBM) is a methods for explaining neural networks. In CBM, concepts which correspond to reasons of outputs are inserted in the last intermediate layer as o…
Bayesian Generalization Error in Linear Neural Networks with Concept Bottleneck Structure and Multitask Formulation
Naoki Hayashi, Yoshihide Sawada
Concept bottleneck model (CBM) is a ubiquitous method that can interpret neural networks using concepts. In CBM, concepts are inserted between the output layer and the last interme…
The Exact Asymptotic Form of Bayesian Generalization Error in Latent Dirichlet Allocation
Naoki Hayashi
Latent Dirichlet allocation (LDA) obtains essential information from data by using Bayesian inference. It is applied to knowledge discovery via dimension reducing and clustering in…
Variational Approximation Error in Bayesian Non-negative Matrix Factorization
Naoki Hayashi
Non-negative matrix factorization (NMF) is a knowledge discovery method that is used in many fields. Variational inference and Gibbs sampling methods for it are also wellknown. How…
Asymptotic Bayesian Generalization Error in Latent Dirichlet Allocation and Stochastic Matrix Factorization
Naoki Hayashi, Sumio Watanabe
Latent Dirichlet allocation (LDA) is useful in document analysis, image processing, and many information systems; however, its generalization performance has been left unknown beca…
Upper Bound of Bayesian Generalization Error in Non-negative Matrix Factorization
Naoki Hayashi, Sumio Watanabe
Non-negative matrix factorization (NMF) is a new knowledge discovery method that is used for text mining, signal processing, bioinformatics, and consumer analysis. However, its bas…