22 citations · 148 across the 31 of their papers we have counts for
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stat.ML2020★ 4 cited
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…
stat.ML2020★ 9 cited
A Generalization of Spatial Monte Carlo Integration
Muneki Yasuda, Kei Uchizawa
Spatial Monte Carlo integration (SMCI) is an extension of standard Monte Carlo integration and can approximate expectations on Markov random fields with high accuracy. SMCI was app…
stat.ML2020★ 2 cited
Consistent Batch Normalization for Weighted Loss in Imbalanced-Data Environment
Muneki Yasuda, Yeo Xian En, Seishirou Ueno
In this study, classification problems based on feedforward neural networks in a data-imbalanced environment are considered. Learning from an imbalanced dataset is one of the most…