4 citations · 5 across the 6 of their papers we have counts for
6 papers
Nonlocal Transition Kernel for Efficient Learning of Restricted Boltzmann Machines
Kaiji Sekimoto, Muneki Yasuda
Learning restricted Boltzmann machines (RBMs) is computationally challenging because it requires expectations whose exact evaluation is generally intractable. The expectations are…
EB-RANSAC: Random Sample Consensus based on Energy-Based Model
Muneki Yasuda, Nao Watanabe, Kaiji Sekimoto
Random sample consensus (RANSAC), which is based on a repetitive sampling from a given dataset, is one of the most popular robust estimation methods. In this study, an energy-based…
Effective Method for Inverse Ising Problem under Missing Observations in Restricted Boltzmann Machines
Kaiji Sekimoto, Muneki Yasuda
Restricted Boltzmann machines (RBMs) are energy-based models analogous to the Ising model and are widely applied in statistical machine learning. The standard inverse Ising problem…
Improving Interpretability of Scores in Anomaly Detection Based on Gaussian-Bernoulli Restricted Boltzmann Machine
Kaiji Sekimoto, Muneki Yasuda
Gaussian-Bernoulli restricted Boltzmann machines (GBRBMs) are often used for semi-supervised anomaly detection, where they are trained using only normal data points. In GBRBM-based…
Composite Spatial Monte Carlo Integration Based on Generalized Least Squares
Kaiji Sekimoto, Muneki Yasuda
Although evaluation of the expectations on the Ising model is essential in various applications, it is mostly infeasible because of intractable multiple summations. Spatial Monte C…
Spatial Monte Carlo Integration with Annealed Importance Sampling
Muneki Yasuda, Kaiji Sekimoto
Evaluating expectations on an Ising model (or Boltzmann machine) is essential for various applications, including statistical machine learning. However, in general, the evaluation…