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pro vyhledávání: '"Kim, JaYoung"'
Structured data, which constitutes a significant portion of existing data types, has been a long-standing research topic in the field of machine learning. Various representation learning methods for tabular data have been proposed, ranging from encod
Externí odkaz:
http://arxiv.org/abs/2312.07753
Autor:
Choi, Jeongwhan, Wi, Hyowon, Kim, Jayoung, Shin, Yehjin, Lee, Kookjin, Trask, Nathaniel, Park, Noseong
Transformers, renowned for their self-attention mechanism, have achieved state-of-the-art performance across various tasks in natural language processing, computer vision, time-series modeling, etc. However, one of the challenges with deep Transforme
Externí odkaz:
http://arxiv.org/abs/2312.04234
With growing attention to tabular data these days, the attempt to apply a synthetic table to various tasks has been expanded toward various scenarios. Owing to the recent advances in generative modeling, fake data generated by tabular data synthesis
Externí odkaz:
http://arxiv.org/abs/2304.12654
Tabular data synthesis is a long-standing research topic in machine learning. Many different methods have been proposed over the past decades, ranging from statistical methods to deep generative methods. However, it has not always been successful due
Externí odkaz:
http://arxiv.org/abs/2210.04018
Tabular data typically contains private and important information; thus, precautions must be taken before they are shared with others. Although several methods (e.g., differential privacy and k-anonymity) have been proposed to prevent information lea
Externí odkaz:
http://arxiv.org/abs/2208.08114
Autor:
Kim, Jayoung, Lee, Chaejeong, Shin, Yehjin, Park, Sewon, Kim, Minjung, Park, Noseong, Cho, Jihoon
Score-based generative models (SGMs) are a recent breakthrough in generating fake images. SGMs are known to surpass other generative models, e.g., generative adversarial networks (GANs) and variational autoencoders (VAEs). Being inspired by their big
Externí odkaz:
http://arxiv.org/abs/2206.08555
Autor:
Lee, Jaehoon, Jeon, Jinsung, Jhin, Sheo yon, Hyeong, Jihyeon, Kim, Jayoung, Jo, Minju, Seungji, Kook, Park, Noseong
The problem of processing very long time-series data (e.g., a length of more than 10,000) is a long-standing research problem in machine learning. Recently, one breakthrough, called neural rough differential equations (NRDEs), has been proposed and h
Externí odkaz:
http://arxiv.org/abs/2204.08781
Autor:
Jhin, Sheo Yon, Lee, Jaehoon, Jo, Minju, Kook, Seungji, Jeon, Jinsung, Hyeong, Jihyeon, Kim, Jayoung, Park, Noseong
Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks. Among them, time-series modeling with neural controlled differential equations (NCDEs) is con
Externí odkaz:
http://arxiv.org/abs/2204.08771
Publikováno v:
In Sensors and Actuators: A. Physical 1 July 2024 372
Autor:
Lee, Yoowoo1 (AUTHOR) youfmc@snu.ac.kr, Kim, Jayoung2 (AUTHOR) marsb612@snu.ac.kr, Mah, Sunghyuck2 (AUTHOR) ryanshmah@snu.ac.kr, Karr, Angela2 (AUTHOR) akarr049@snu.ac.kr
Publikováno v:
Entrepreneurship Research Journal. Jul2024, Vol. 14 Issue 3, p905-950. 46p.