no code implementations • 24 May 2024 • Futoshi Futami, Masahiro Fujisawa
While the expected calibration error (ECE), which employs binning, is widely adopted to evaluate the calibration performance of machine learning models, theoretical understanding of its estimation bias is limited.
no code implementations • 10 Oct 2023 • Takeshi Koshizuka, Masahiro Fujisawa, Yusuke Tanaka, Issei Sato
Building upon this observation, we also propose an edge of chaos initialization scheme for FNO to mitigate the negative initialization bias leading to training instability.
no code implementations • 13 Jun 2020 • Masahiro Fujisawa, Takeshi Teshima, Issei Sato, Masashi Sugiyama
Approximate Bayesian computation (ABC) is a likelihood-free inference method that has been employed in various applications.
no code implementations • 1 Feb 2019 • Masahiro Fujisawa, Issei Sato
We theoretically show that, with our method, the variance of the gradient estimator decreases as optimization proceeds and that a learning rate scheduler function helps improve the convergence.