5 citations · 12 across the 4 of their papers we have counts for
4 papers
Detecting Signs of Model Change with Continuous Model Selection Based on Descriptive Dimensionality
Kenji Yamanishi, So Hirai
We address the issue of detecting changes of models that lie behind a data stream. The model refers to an integer-valued structural information such as the number of free parameter…
Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error
Taiji Suzuki, Hiroshi Abe, Tomoya Murata +6
Compression techniques for deep neural network models are becoming very important for the efficient execution of high-performance deep learning systems on edge-computing devices. T…
Upper Bound on Normalized Maximum Likelihood Codes for Gaussian Mixture Models
So Hirai, Kenji Yamanishi
This paper shows that the normalized maximum likelihood~(NML) code-length calculated in [1] is an upper bound on the NML code-length strictly calculated for the Gaussian Mixture Mo…
Normalized Maximum Likelihood Coding for Exponential Family with Its Applications to Optimal Clustering
So Hirai, Kenji Yamanishi
We are concerned with the issue of how to calculate the normalized maximum likelihood (NML) code-length. There is a problem that the normalization term of the NML code-length may d…