Zobrazeno 1 - 10
of 18 003
pro vyhledávání: '"Lee, In‐Kyu"'
We study the high-dimensional partial linear model, where the linear part has a high-dimensional sparse regression coefficient and the nonparametric part includes a function whose derivatives are of bounded total variation. We expand upon the univari
Externí odkaz:
http://arxiv.org/abs/2410.20319
We mimic the conventional explicit Total Variation Diminishing Runge-Kutta (TVDRK) schemes and propose a class of numerical integrators to solve differential equations on a unit sphere. Our approach utilizes the exponential map inherent to the sphere
Externí odkaz:
http://arxiv.org/abs/2410.10420
In the training and inference of spiking neural networks (SNNs), direct training and lightweight computation methods have been orthogonally developed, aimed at reducing power consumption. However, only a limited number of approaches have applied thes
Externí odkaz:
http://arxiv.org/abs/2408.12293
Addressing health disparities among different demographic groups is a key challenge in public health. Despite many efforts, there is still a gap in understanding how these disparities unfold over time. Our paper focuses on this overlooked longitudina
Externí odkaz:
http://arxiv.org/abs/2404.11675
Publikováno v:
PMLR 202:19010-19035, 2023
Transfer learning is a de facto standard method for efficiently training machine learning models for data-scarce problems by adding and fine-tuning new classification layers to a model pre-trained on large datasets. Although numerous previous studies
Externí odkaz:
http://arxiv.org/abs/2403.14111
Machine unlearning aims to eliminate the influence of a subset of training samples (i.e., unlearning samples) from a trained model. Effectively and efficiently removing the unlearning samples without negatively impacting the overall model performance
Externí odkaz:
http://arxiv.org/abs/2401.10458
Autor:
Lee, Jisun, Kwon, Jay Hyoun, Park, Chang Yong, Kim, Huidong, Choi, In-Mook, Chung, Jin Wan, Lee, Won-Kyu
Relativistic redshift correction should be accurately considered in frequency comparisons between frequency standards. In this study, we evaluated the relativistic redshift at Korea Research Institute of Standards and Science (KRISS) using three diff
Externí odkaz:
http://arxiv.org/abs/2401.04943
Accurate segmentation of coronary arteries is a pivotal process in assessing cardiovascular diseases. However, the intricate structure of the cardiovascular system presents significant challenges for automatic segmentation, especially when utilizing
Externí odkaz:
http://arxiv.org/abs/2311.10306
Coronary artery stenosis is a critical health risk, and its precise identification in Coronary Angiography (CAG) can significantly aid medical practitioners in accurately evaluating the severity of a patient's condition. The complexity of coronary ar
Externí odkaz:
http://arxiv.org/abs/2311.10281
Autor:
Park, Soohyun, Yun, Won Joon, Park, Chanyoung, Lee, Youn Kyu, Jung, Soyi, Feng, Hao, Kim, Joongheon
This paper proposes an efficient quantum train engine (EQuaTE), a novel tool for quantum machine learning software which plots gradient variances to check whether our quantum neural network (QNN) falls into local minima (called barren plateaus in QNN
Externí odkaz:
http://arxiv.org/abs/2302.03853