no code implementations • 30 May 2023 • Miya Nakajima, Takahiro Saitoh, Tsuyoshi Kato
In recent years, laser ultrasonic visualization testing (LUVT) has attracted much attention because of its ability to efficiently perform non-contact ultrasonic non-destructive testing. Despite many success reports of deep learning based image analysis for widespread areas, attempts to apply deep learning to defect detection in LUVT images face the difficulty of preparing a large dataset of LUVT images that is too expensive to scale.
no code implementations • 23 May 2023 • Miya Nakajima, Takahiro Saitoh, Tsuyoshi Kato
The importance of ultrasonic nondestructive testing has been increasing in recent years, and there are high expectations for the potential of laser ultrasonic visualization testing, which combines laser ultrasonic testing with scattered wave visualization technology.
no code implementations • 21 Feb 2023 • Yuya Takada, Tsuyoshi Kato
One drawback of the Tobit model is that only the target variable is allowed to be censored.
no code implementations • 5 Jan 2021 • Kenya Tajima, Takahiko Henmi, Kohei Tsuchida, Esmeraldo Ronnie R. Zara, Tsuyoshi Kato
One of the two algorithms is based on the projected gradient method, in which each iteration of the projected gradient method takes $O(nd)$ computational cost and the sublinear convergence of the objective error is guaranteed.
no code implementations • 20 Aug 2020 • Kenya Tajima, Yoshihiro Hirohashi, Esmeraldo Ronnie Rey Zara, Tsuyoshi Kato
In this study, we developed a new optimization algorithm that can be applied to many of MC-SVM variants.
no code implementations • 16 Aug 2020 • Takahiko Henmi, Esmeraldo Ronnie Rey Zara, Yoshihiro Hirohashi, Tsuyoshi Kato
In this study, we derived the initialization scheme again not from the simplified Kaiming model, but precisely from the modern CNN architectures, and empirically investigated how the new initialization method performs compared to the de facto standard ones that are widely used today.
no code implementations • 17 Apr 2018 • Rachelle Rivero, Tsuyoshi Kato
In view of this limitation, our proposed methods employ the LogDet divergence, which ensures the positive definiteness of the resulting inferred kernel matrix.
no code implementations • 7 Jan 2018 • Yuya Onuma, Rachelle Rivero, Tsuyoshi Kato
In this study, an efficient technique that can solve the nonlinear equation in $O(Mn^{3})$ has been discovered.
no code implementations • 12 Oct 2017 • Tsuyoshi Kato, Misato Kobayashi, Daisuke Sano
In this paper, we introduce sign constraints that are a handy and simple representation for non-experts in generic learning problems.
no code implementations • 14 Feb 2017 • Tsuyoshi Kato, Rachelle Rivero
In this paper, we present a new method, called Mutual Kernel Matrix Completion (MKMC) algorithm, that tackles this problem of mutually inferring the missing entries of multiple kernel matrices by combining the notions of data fusion and kernel matrix completion, applied on biological data sets to be used for classification task.
no code implementations • 7 Jan 2016 • Tomoki Matsuzawa, Raissa Relator, Jun Sese, Tsuyoshi Kato
Recently, covariance descriptors have received much attention as powerful representations of set of points.
no code implementations • 19 Jun 2015 • Tsuyoshi Kato, Raissa Relator, Hayliang Ngouv, Yoshihiro Hirohashi, Tetsuhiro Kakimoto, Kinya Okada
A new descriptor referred to as Segmental HOG is developed to perform a comprehensive detection of hundreds of glomeruli in images of whole kidney sections.
no code implementations • NeurIPS 2007 • Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, Kiyoshi Asai
In this paper, we propose a novel MTL algorithm that can overcome these problems.