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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…