Zobrazeno 1 - 10
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pro vyhledávání: '"Kim, Dongha"'
Autor:
Kim, Donghan
This thesis generalizes stochastic portfolio theory in two different aspects. The first part demonstrates the functional generation of portfolios in a pathwise way. This notion of functional generation of portfolios was first introduced by E.R. Fernh
Goal-oriented chatbots are essential for automating user tasks, such as booking flights or making restaurant reservations. A key component of these systems is Dialogue State Tracking (DST), which interprets user intent and maintains the dialogue stat
Externí odkaz:
http://arxiv.org/abs/2410.22767
Autor:
Park, Sang Hyun, Koh, Jun Young, Lee, Junha, Song, Joy, Kim, Dongha, Moon, Hoyeon, Lee, Hyunju, Song, Min
In this work, we share the insights for achieving state-of-the-art quality in our text-to-image anime image generative model, called Illustrious. To achieve high resolution, dynamic color range images, and high restoration ability, we focus on three
Externí odkaz:
http://arxiv.org/abs/2409.19946
Space-time wavepackets (STWPs) have received significant attention since they can propagate in free space at arbitrary group velocity without dispersion and diffraction. However, at present, the generation of STWPs has been limited to the paraxial re
Externí odkaz:
http://arxiv.org/abs/2409.10454
ALTBI: Constructing Improved Outlier Detection Models via Optimization of Inlier-Memorization Effect
Outlier detection (OD) is the task of identifying unusual observations (or outliers) from a given or upcoming data by learning unique patterns of normal observations (or inliers). Recently, a study introduced a powerful unsupervised OD (UOD) solver b
Externí odkaz:
http://arxiv.org/abs/2408.09791
There are two things to be considered when we evaluate predictive models. One is prediction accuracy,and the other is interpretability. Over the recent decades, many prediction models of high performance, such as ensemble-based models and deep neural
Externí odkaz:
http://arxiv.org/abs/2408.00973
Autor:
Lee, Changwon, Araujo, Israel F., Kim, Dongha, Lee, Junghan, Park, Siheon, Ryu, Ju-Young, Park, Daniel K.
Quantum convolutional neural networks (QCNNs) represent a promising approach in quantum machine learning, paving new directions for both quantum and classical data analysis. This approach is particularly attractive due to the absence of the barren pl
Externí odkaz:
http://arxiv.org/abs/2403.19099
Publikováno v:
ACS Photonics 11, 2379 (2024)
Surface plasmon polaritons (SPPs) carry transverse optical spin within the evanescent field, which has enabled the demonstration of various chiral light-matter interactions in classical and quantum systems. To achieve high spin selectivity in the int
Externí odkaz:
http://arxiv.org/abs/2402.08728
Autor:
Kim, Dongha
Oxides have been extensively used as high temperature catalysts in electrochemical devices and for gas conversion reactions, including solid oxide fuel cells (SOFC), solid oxide electrolysis cells (SOEC), ethane cracking, CO oxidation, and the oxidat
Externí odkaz:
https://hdl.handle.net/1721.1/153076