Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Sakata, Ayaka"'
This paper examines the quantization methods used in large-scale data analysis models and their hyperparameter choices. The recent surge in data analysis scale has significantly increased computational resource requirements. To address this, quantizi
Externí odkaz:
http://arxiv.org/abs/2401.17269
Autor:
Sakata, Ayaka, Kaneko, Kunihiko
Biological systems must be robust for stable function against perturbations, but robustness alone is not sufficient. The ability to switch between appropriate states (phenotypes) in response to different conditions is essential for biological functio
Externí odkaz:
http://arxiv.org/abs/2304.11437
Autor:
Sakata, Ayaka
We discuss the prediction accuracy of assumed statistical models in terms of prediction errors for the generalized linear model and penalized maximum likelihood methods. We derive the forms of estimators for the prediction errors, such as $C_p$ crite
Externí odkaz:
http://arxiv.org/abs/2206.12832
Autor:
Sakata, Ayaka, Kabashima, Yoshiyuki
We study the inference problem in the group testing to identify defective items from the perspective of the decision theory. We introduce Bayesian inference and consider the Bayesian optimal setting in which the true generative process of the test re
Externí odkaz:
http://arxiv.org/abs/2110.10877
Autor:
Sakata, Ayaka
Publikováno v:
Phys. Rev. E 103, 022110 (2021)
In identifying infected patients in a population, group testing is an effective method to reduce the number of tests and correct the test errors. In the group testing procedure, tests are performed on pools of specimens collected from patients, where
Externí odkaz:
http://arxiv.org/abs/2007.13323
Autor:
Sakata, Ayaka
Group testing is a method of identifying infected patients by performing tests on a pool of specimens collected from patients. For the case in which the test returns a false result with finite probability, we propose Bayesian inference and a correspo
Externí odkaz:
http://arxiv.org/abs/2004.13667
Autor:
Sakata, Ayaka, Kaneko, Kunihiko
The evolution of high-dimensional phenotypes is investigated using a statistical physics model consists of interacting spins, in which genotypes, phenotypes, and environments are represented by spin configurations, interaction matrices, and external
Externí odkaz:
http://arxiv.org/abs/2001.03714
Autor:
Obuchi, Tomoyuki, Sakata, Ayaka
We investigate the signal reconstruction performance of sparse linear regression in the presence of noise when piecewise continuous nonconvex penalties are used. Among such penalties, we focus on the SCAD penalty. The contributions of this study are
Externí odkaz:
http://arxiv.org/abs/1902.10375
Autor:
Sakata, Ayaka, Obuchi, Tomoyuki
We consider compressed sensing formulated as a minimization problem of nonconvex sparse penalties, Smoothly Clipped Absolute deviation (SCAD) and Minimax Concave Penalty (MCP). The nonconvexity of these penalties is controlled by nonconvexity paramet
Externí odkaz:
http://arxiv.org/abs/1902.07436
Autor:
Sakata, Ayaka
We propose an estimator of prediction error using an approximate message passing (AMP) algorithm that can be applied to a broad range of sparse penalties. Following Stein's lemma, the estimator of the generalized degrees of freedom, which is a key qu
Externí odkaz:
http://arxiv.org/abs/1802.06939