4 papers · 1 filter
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…
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…