no code implementations • 23 Feb 2024 • Sungwoo Park, Junyeop Kwon, Byeongnoh Kim, Suhyun Chae, Jeeyong Lee, Dabeen Lee
We provide experimental results that demonstrate the numerical superiority of our algorithms over the existing method and other black-box optimistic optimization methods.
1 code implementation • 30 Sep 2023 • Yulu Gan, Sungwoo Park, Alexander Schubert, Anthony Philippakis, Ahmed M. Alaa
We then use a large language model to paraphrase prompt templates that convey the specific tasks to be conducted on each image, and through this process, we create a multi-modal and multi-task training dataset comprising input and output images along with annotated instructions.
no code implementations • 8 Sep 2023 • Sungjun Cho, Seunghyuk Cho, Sungwoo Park, Hankook Lee, Honglak Lee, Moontae Lee
Real-world graphs naturally exhibit hierarchical or cyclical structures that are unfit for the typical Euclidean space.
no code implementations • 2nd Annual Topology, Algebra, and Geometry in Machine Learning Workshop 2023 • Sungjun Cho, Seunghyuk Cho, Sungwoo Park, Hankook Lee, Honglak Lee, Moontae Lee
Real-world graphs naturally exhibit hierarchical or cyclical structures that are unfit for the typical Euclidean space.
1 code implementation • 14 Mar 2023 • Geumbyeol Hwang, Sunwon Hong, SeungHyun Lee, Sungwoo Park, Gyeongsu Chae
We enhance the efficiency of DisCoHead by integrating a dense motion estimator and the encoder of a generator which are originally separate modules.
no code implementations • 6 Jul 2022 • Jungyu Ahn, Sungwoo Park, Jiwoon Kim, Ju-Hong Lee
Second, Monte Carlo simulation data are used to increase training data complexity to prevent model overfitting.
no code implementations • 4 Jul 2022 • Jinho Lee, Sungwoo Park, Jungyu Ahn, Jonghun Kwak
Therefore, we use the data of individual stocks to train our neural networks to predict the future performance of individual stocks and use these predictions and the portfolio deposit file (PDF) to construct a portfolio of ETFs.
no code implementations • 1 Jul 2022 • Jonghun Kwak, Jungyu Ahn, Jinho Lee, Sungwoo Park
The finance industry has adopted machine learning (ML) as a form of quantitative research to support better investment decisions, yet there are several challenges often overlooked in practice.
no code implementations • ICCV 2021 • Patrick Kwon, Jaeseong You, Gyuhyeon Nam, Sungwoo Park, Gyeongsu Chae
A variety of effective face-swap and face-reenactment methods have been publicized in recent years, democratizing the face synthesis technology to a great extent.
no code implementations • 9 Mar 2021 • Sungwoo Park, Rajan Gupta, Boram Yoon, Santanu Mondal, Tanmoy Bhattacharya, Yong-Chull Jang, Bálint Joó, Frank Winter
Similarly, we find evidence that the $N\pi\pi $ excited state contributes to the correlation functions with the vector current, consistent with the vector meson dominance model.
High Energy Physics - Lattice High Energy Physics - Phenomenology
no code implementations • 24 Nov 2020 • Santanu Mondal, Rajan Gupta, Sungwoo Park, Boram Yoon, Tanmoy Bhattacharya, Bálint Joó, Frank Winter
Our final results, in the $\overline{\rm MS}$ scheme at 2 GeV, are $\langle x \rangle_{u-d} = 0. 160(16)(20)$, $\langle x \rangle_{\Delta u-\Delta d} = 0. 192(13)(20)$ and $\langle x \rangle_{\delta u-\delta d} = 0. 215(17)(20)$, where the first error is the overall analysis uncertainty assuming excited-state contributions have been removed, and the second is an additional systematic uncertainty due to possible residual excited-state contributions.
High Energy Physics - Lattice