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pro vyhledávání: '"Kaiji Sekimoto"'
Autor:
Kaiji Sekimoto, Muneki Yasuda
Publikováno v:
Nonlinear Theory and Its Applications, IEICE. 14:228-241
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7879aed967aef4b630002c4e16d0cf5a
https://doi.org/10.1587/nolta.14.228
https://doi.org/10.1587/nolta.14.228
Autor:
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 Carlo integration (SMCI) is a sampling-based approximation. It can prov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e23bf3a99362be77711a7835ab8ae38e
Autor:
Kaiji Sekimoto, Muneki Yasuda
Publikováno v:
Physical review. E. 103(5-1)
Evaluating expectations on an Ising model (or Boltzmann machine) is essential for various applications, including statistical machine learning. However, in general, the evaluation is computationally difficult because it involves intractable multiple
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::06c85d2eacc440b096a667271b3acbd0
https://pubmed.ncbi.nlm.nih.gov/34134233
https://pubmed.ncbi.nlm.nih.gov/34134233