3 papers
stat.ML2026
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…
stat.ML2026
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…
stat.ML2025
Dataset-Free Weight-Initialization on Restricted Boltzmann Machine
Muneki Yasuda, Ryosuke Maeno, Chako Takahashi
In feed-forward neural networks, dataset-free weight-initialization methods such as LeCun, Xavier (or Glorot), and He initializations have been developed. These methods randomly de…