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Found 13 papers, 0 papers with code
Simulation-Aided Deep Learning for Laser Ultrasonic Visualization Testing
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.
A Study on Deep CNN Structures for Defect Detection From Laser Ultrasonic Visualization Testing Images
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.
Multi-Target Tobit Models for Completing Water Quality Data
One drawback of the Tobit model is that only the target variable is allowed to be censored.
Learning Sign-Constrained Support Vector Machines
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.
Frank-Wolfe algorithm for learning SVM-type multi-category classifiers
In this study, we developed a new optimization algorithm that can be applied to many of MC-SVM variants.
Adaptive Signal Variances: CNN Initialization Through Modern Architectures
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.
Parametric Models for Mutual Kernel Matrix Completion
In view of this limitation, our proposed methods employ the LogDet divergence, which ensures the positive definiteness of the resulting inferred kernel matrix.
Threshold Auto-Tuning Metric Learning
In this study, an efficient technique that can solve the nonlinear equation in $O(Mn^{3})$ has been discovered.
Sign-Constrained Regularized Loss Minimization
In this paper, we introduce sign constraints that are a handy and simple representation for non-experts in generic learning problems.
Mutual Kernel Matrix Completion
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.
Stochastic Dykstra Algorithms for Metric Learning on Positive Semi-Definite Cone
Recently, covariance descriptors have received much attention as powerful representations of set of points.
New Descriptor for Glomerulus Detection in Kidney Microscopy Image
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.
Multi-Task Learning via Conic Programming
In this paper, we propose a novel MTL algorithm that can overcome these problems.
