노영균 교수

This paper shows how metric learning can be used with Nadaraya-Watson (NW) kernel regression. Compared with standard approaches, such as bandwidth selection, we show how metric learning can significantly reduce the mean square error (MSE) in kernel regression, particularly for high-dimensional data. We propose a method for efficiently learning a good metric function based upon analyzing the performance of the NW estimator for Gaussian-distributed data. A key feature of our approach is that the N

A new approach to $L_2$-consistent estimation of a general density functional using $k$-nearest neighbor distances is proposed, where the functional under consideration is in the form of the expectation of some function $f$ of the densities at each point. The estimator is designed to be asymptotically unbiased, using the convergence of the normalized volume of a $k$-nearest neighbor ball to a Gamma distribution in the large-sample limit, and naturally involves the inverse Laplace transform of a

Minimax optimization plays a key role in adversarial training of machine learning algorithms, such as learning generative models, domain adaptation, privacy preservation, and robust learning. In this paper, we demonstrate the failure of alternating gradient descent in minimax optimization problems due to the discontinuity of solutions of the inner maximization. To address this, we propose a new epsilon-subgradient descent algorithm that addresses this problem by simultaneously tracking K candida

This study aimed to develop and evaluate deep convolutional neural network (DCNN) models with Grad-CAM visualization for the automated classification with interpretability of tongue conditions-specifically glossitis and oral squamous cell carcinoma (OSCC)-using clinical tongue photographs, with a focus on their potential for early detection and telemedicine-based diagnostics. A total of 652 tongue images were categorized into normal control (n = 294), glossitis (n = 340), and OSCC (n = 17). Four

We consider the problem of reducing the dimensionality of labeled data for classification. Unfortunately, the optimal approach of finding the low-dimensional projection with minimal Bayes classification error is intractable, so most standard algorithms optimize a tractable heuristic function in the projected subspace. Here, we investigate a physics-based model where we consider the labeled data as interacting fluid distributions. We derive the forces arising in the fluids from information theore

The effect of a low-temperature AlN strain relaxation layer on the strain state and the leakage characteristics of GaN buffer layers grown on (110) Si substrates by using ammonia molecular beam epitaxy has been investigated. Excess charge accumulation at the position of LT-AlN strain relaxation layer is found to result in a leakage current through the GaN buffer layer that is a few orders of magnitude higher than through the GaN buffer layer without the LT-AlN strain relaxation layer. An approac

AlN seed layers with a thickness of 50 nm were grown by using nitrogen plasma-assisted molecularbeam epitaxy on (110) Si substrates with different V/III ratios in the temperature range from 850C to 940 C. In varying the Al/N ratio and the growth temperature, distinct surface morphologiesemerge, which are quite different from those observed in AlN growth on (111) Si substrates. UnderN-rich conditions, AlN films exhibits randomly distributed islands with different sizes rangingfrom 10 nm to 1 m. I