464 citations · 538 across the 15 of their papers we have counts for
16 papers
Reconsidering Dependency Networks from an Information Geometry Perspective
Kazuya Takabatake, Shotaro Akaho
Dependency networks (Heckerman et al., 2000) provide a flexible framework for modeling complex systems with many variables by combining independently learned local conditional dist…
A New Framework for Multi-Line Analysis Combined Kernel PCA and Kernel SHAP: A Case of NGC 1068 ALMA Band 3 Data
Hiroma Okubo, Tsutomu T. Takeuchi, Shotaro Akaho +7
We present a new framework for multi-line analysis that combines kernel principal component analysis (Kernel PCA), an unsupervised machine-learning method, and Kernel SHapley Addit…
Geometry of EM and related iterative algorithms
Hideitsu Hino, Shotaro Akaho, Noboru Murata
The Expectation--Maximization (EM) algorithm is a simple meta-algorithm that has been used for many years as a methodology for statistical inference when there are missing measurem…
Full-Span Log-Linear Model and Fast Learning Algorithm
Kazuya Takabatake, Shotaro Akaho
The full-span log-linear(FSLL) model introduced in this paper is considered an -th order Boltzmann machine, where is the number of all variables in the target system. Let $X…
Learning Curves for Continual Learning in Neural Networks: Self-Knowledge Transfer and Forgetting
Ryo Karakida, Shotaro Akaho
Sequential training from task to task is becoming one of the major objects in deep learning applications such as continual learning and transfer learning. Nevertheless, it remains…
Reconsidering Dependency Networks from an Information Geometry Perspective
Kazuya Takabatake, Shotaro Akaho
Dependency networks (Heckerman et al., 2000) are potential probabilistic graphical models for systems comprising a large number of variables. Like Bayesian networks, the structure…