Zobrazeno 1 - 10
of 135
pro vyhledávání: '"TAKENOUCHI, Takashi"'
This paper discusses the problem of weakly supervised classification, in which instances are given weak labels that are produced by some label-corruption process. The goal is to derive conditions under which loss functions for weak-label learning are
Externí odkaz:
http://arxiv.org/abs/2103.02893
Autor:
Sasaki, Hiroaki, Takenouchi, Takashi
Contrastive learning is a recent promising approach in unsupervised representation learning where a feature representation of data is learned by solving a pseudo classification problem from unlabelled data. However, it is not straightforward to under
Externí odkaz:
http://arxiv.org/abs/2101.02083
Predicting which action (treatment) will lead to a better outcome is a central task in decision support systems. To build a prediction model in real situations, learning from biased observational data is a critical issue due to the lack of randomized
Externí odkaz:
http://arxiv.org/abs/2006.05616
Nonlinear independent component analysis (ICA) is a general framework for unsupervised representation learning, and aimed at recovering the latent variables in data. Recent practical methods perform nonlinear ICA by solving a series of classification
Externí odkaz:
http://arxiv.org/abs/1911.00265
In this paper, we propose a novel domain adaptation method that can be applied without target data. We consider the situation where domain shift is caused by a prior change of a specific factor and assume that we know how the prior changes between so
Externí odkaz:
http://arxiv.org/abs/1903.05312
The parameter estimation of unnormalized models is a challenging problem. The maximum likelihood estimation (MLE) is computationally infeasible for these models since normalizing constants are not explicitly calculated. Although some consistent estim
Externí odkaz:
http://arxiv.org/abs/1901.07710
Publikováno v:
In Expert Systems With Applications 1 September 2022 201
One of the most common methods for statistical inference is the maximum likelihood estimator (MLE). The MLE needs to compute the normalization constant in statistical models, and it is often intractable. Using unnormalized statistical models and repl
Externí odkaz:
http://arxiv.org/abs/1604.06568
Autor:
Sasaki, Hiroaki, Takenouchi, Takashi
Publikováno v:
Japanese Journal of Statistics & Data Science; Jun2024, Vol. 7 Issue 1, p223-252, 30p