Zobrazeno 1 - 10
of 321
pro vyhledávání: '"Kim JaeKwang"'
Dataset bias is a significant challenge in machine learning, where specific attributes, such as texture or color of the images are unintentionally learned resulting in detrimental performance. To address this, previous efforts have focused on debiasi
Externí odkaz:
http://arxiv.org/abs/2406.06134
This paper presents a formulation for deterministically calculating optimized paths for a multiagent system consisting of heterogeneous vehicles. The key idea is the calculation of the shortest time for each agent to reach every grid point from its k
Externí odkaz:
http://arxiv.org/abs/2310.14507
Autor:
Kim, Jaekwang, Admal, Nikhil Chandra
This paper investigates the statistical behavior of two-dimensional grain microstructures during grain growth under anisotropic grain boundary characters. We employ the threshold-dynamics method, which allows for unparalleled computational speed, to
Externí odkaz:
http://arxiv.org/abs/2309.09243
Sequential recommendation addresses the issue of preference drift by predicting the next item based on the user's previous behaviors. Recently, a promising approach using contrastive learning has emerged, demonstrating its effectiveness in recommendi
Externí odkaz:
http://arxiv.org/abs/2308.03400
Publikováno v:
In Journal of Controlled Release October 2024 374:525-537
Autor:
Bae, Joong Ho, Hwang, Keebum, Kim, Jaekwang, Kang, Hyunchul, Lee, Ilbok, Park, Chul Wan, Sohn, Hiesang, Yoon, Songhun
Publikováno v:
In Journal of Electroanalytical Chemistry 15 March 2024 957
One of the most important aims of grain boundary modeling is to predict the evolution of a large collection of grains in phenomena such as abnormal grain growth, coupled grain boundary motion, and recrystallization that occur under extreme thermomech
Externí odkaz:
http://arxiv.org/abs/2102.02773
In this paper, we propose Edge Profile Super Resolution(EPSR) method to preserve structure information and to restore texture. We make EPSR by stacking modified Fractal Residual Network(mFRN) structures hierarchically and repeatedly. mFRN is made up
Externí odkaz:
http://arxiv.org/abs/2011.05308
A recommender system generates personalized recommendations for a user by computing the preference score of items, sorting the items according to the score, and filtering top-K items with high scores. While sorting and ranking items are integral for
Externí odkaz:
http://arxiv.org/abs/2008.13141
Publikováno v:
In Neuropharmacology 1 November 2023 238