Search Results for author: Jonghyun Lee

Found 23 papers, 13 papers with code

Multi-fidelity Hamiltonian Monte Carlo

no code implementations8 May 2024 Dhruv V. Patel, Jonghyun Lee, Matthew W. Farthing, Peter K. Kitanidis, Eric F. Darve

In this multi-fidelity algorithm, the acceptance probability is computed in the first stage via a standard HMC proposal using an inexpensive differentiable surrogate model, and if the proposal is accepted, the posterior is evaluated in the second stage using the high-fidelity (HF) numerical solver.

HyperCLOVA X Technical Report

no code implementations2 Apr 2024 Kang Min Yoo, Jaegeun Han, Sookyo In, Heewon Jeon, Jisu Jeong, Jaewook Kang, Hyunwook Kim, Kyung-Min Kim, Munhyong Kim, Sungju Kim, Donghyun Kwak, Hanock Kwak, Se Jung Kwon, Bado Lee, Dongsoo Lee, Gichang Lee, Jooho Lee, Baeseong Park, Seongjin Shin, Joonsang Yu, Seolki Baek, Sumin Byeon, Eungsup Cho, Dooseok Choe, Jeesung Han, Youngkyun Jin, Hyein Jun, Jaeseung Jung, Chanwoong Kim, jinhong Kim, Jinuk Kim, Dokyeong Lee, Dongwook Park, Jeong Min Sohn, Sujung Han, Jiae Heo, Sungju Hong, Mina Jeon, Hyunhoon Jung, Jungeun Jung, Wangkyo Jung, Chungjoon Kim, Hyeri Kim, Jonghyun Kim, Min Young Kim, Soeun Lee, Joonhee Park, Jieun Shin, Sojin Yang, Jungsoon Yoon, Hwaran Lee, Sanghwan Bae, Jeehwan Cha, Karl Gylleus, Donghoon Ham, Mihak Hong, Youngki Hong, Yunki Hong, Dahyun Jang, Hyojun Jeon, Yujin Jeon, Yeji Jeong, Myunggeun Ji, Yeguk Jin, Chansong Jo, Shinyoung Joo, Seunghwan Jung, Adrian Jungmyung Kim, Byoung Hoon Kim, Hyomin Kim, Jungwhan Kim, Minkyoung Kim, Minseung Kim, Sungdong Kim, Yonghee Kim, Youngjun Kim, Youngkwan Kim, Donghyeon Ko, Dughyun Lee, Ha Young Lee, Jaehong Lee, Jieun Lee, Jonghyun Lee, Jongjin Lee, Min Young Lee, Yehbin Lee, Taehong Min, Yuri Min, Kiyoon Moon, Hyangnam Oh, Jaesun Park, Kyuyon Park, Younghun Park, Hanbae Seo, Seunghyun Seo, Mihyun Sim, Gyubin Son, Matt Yeo, Kyung Hoon Yeom, Wonjoon Yoo, Myungin You, Doheon Ahn, Homin Ahn, Joohee Ahn, Seongmin Ahn, Chanwoo An, Hyeryun An, Junho An, Sang-Min An, Boram Byun, Eunbin Byun, Jongho Cha, Minji Chang, Seunggyu Chang, Haesong Cho, Youngdo Cho, Dalnim Choi, Daseul Choi, Hyoseok Choi, Minseong Choi, Sangho Choi, Seongjae Choi, Wooyong Choi, Sewhan Chun, Dong Young Go, Chiheon Ham, Danbi Han, Jaemin Han, Moonyoung Hong, Sung Bum Hong, Dong-Hyun Hwang, Seongchan Hwang, Jinbae Im, Hyuk Jin Jang, Jaehyung Jang, Jaeni Jang, Sihyeon Jang, Sungwon Jang, Joonha Jeon, Daun Jeong, JoonHyun Jeong, Kyeongseok Jeong, Mini Jeong, Sol Jin, Hanbyeol Jo, Hanju Jo, Minjung Jo, Chaeyoon Jung, Hyungsik Jung, Jaeuk Jung, Ju Hwan Jung, Kwangsun Jung, Seungjae Jung, Soonwon Ka, Donghan Kang, Soyoung Kang, Taeho Kil, Areum Kim, Beomyoung Kim, Byeongwook Kim, Daehee Kim, Dong-Gyun Kim, Donggook Kim, Donghyun Kim, Euna Kim, Eunchul Kim, Geewook Kim, Gyu Ri Kim, Hanbyul Kim, Heesu Kim, Isaac Kim, Jeonghoon Kim, JiHye Kim, Joonghoon Kim, Minjae Kim, Minsub Kim, Pil Hwan Kim, Sammy Kim, Seokhun Kim, Seonghyeon Kim, Soojin Kim, Soong Kim, Soyoon Kim, Sunyoung Kim, TaeHo Kim, Wonho Kim, Yoonsik Kim, You Jin Kim, Yuri Kim, Beomseok Kwon, Ohsung Kwon, Yoo-Hwan Kwon, Anna Lee, Byungwook Lee, Changho Lee, Daun Lee, Dongjae Lee, Ha-Ram Lee, Hodong Lee, Hwiyeong Lee, Hyunmi Lee, Injae Lee, Jaeung Lee, Jeongsang Lee, Jisoo Lee, JongSoo Lee, Joongjae Lee, Juhan Lee, Jung Hyun Lee, Junghoon Lee, Junwoo Lee, Se Yun Lee, Sujin Lee, Sungjae Lee, Sungwoo Lee, Wonjae Lee, Zoo Hyun Lee, Jong Kun Lim, Kun Lim, Taemin Lim, Nuri Na, Jeongyeon Nam, Kyeong-Min Nam, Yeonseog Noh, Biro Oh, Jung-Sik Oh, Solgil Oh, Yeontaek Oh, Boyoun Park, Cheonbok Park, Dongju Park, Hyeonjin Park, Hyun Tae Park, Hyunjung Park, JiHye Park, Jooseok Park, JungHwan Park, Jungsoo Park, Miru Park, Sang Hee Park, Seunghyun Park, Soyoung Park, Taerim Park, Wonkyeong Park, Hyunjoon Ryu, Jeonghun Ryu, Nahyeon Ryu, Soonshin Seo, Suk Min Seo, Yoonjeong Shim, Kyuyong Shin, Wonkwang Shin, Hyun Sim, Woongseob Sim, Hyejin Soh, Bokyong Son, Hyunjun Son, Seulah Son, Chi-Yun Song, Chiyoung Song, Ka Yeon Song, Minchul Song, Seungmin Song, Jisung Wang, Yonggoo Yeo, Myeong Yeon Yi, Moon Bin Yim, Taehwan Yoo, Youngjoon Yoo, Sungmin Yoon, Young Jin Yoon, Hangyeol Yu, Ui Seon Yu, Xingdong Zuo, Jeongin Bae, Joungeun Bae, Hyunsoo Cho, Seonghyun Cho, Yongjin Cho, Taekyoon Choi, Yera Choi, Jiwan Chung, Zhenghui Han, Byeongho Heo, Euisuk Hong, Taebaek Hwang, Seonyeol Im, Sumin Jegal, Sumin Jeon, Yelim Jeong, Yonghyun Jeong, Can Jiang, Juyong Jiang, Jiho Jin, Ara Jo, Younghyun Jo, Hoyoun Jung, Juyoung Jung, Seunghyeong Kang, Dae Hee Kim, Ginam Kim, Hangyeol Kim, Heeseung Kim, Hyojin Kim, Hyojun Kim, Hyun-Ah Kim, Jeehye Kim, Jin-Hwa Kim, Jiseon Kim, Jonghak Kim, Jung Yoon Kim, Rak Yeong Kim, Seongjin Kim, Seoyoon Kim, Sewon Kim, Sooyoung Kim, Sukyoung Kim, Taeyong Kim, Naeun Ko, Bonseung Koo, Heeyoung Kwak, Haena Kwon, Youngjin Kwon, Boram Lee, Bruce W. Lee, Dagyeong Lee, Erin Lee, Euijin Lee, Ha Gyeong Lee, Hyojin Lee, Hyunjeong Lee, Jeeyoon Lee, Jeonghyun Lee, Jongheok Lee, Joonhyung Lee, Junhyuk Lee, Mingu Lee, Nayeon Lee, Sangkyu Lee, Se Young Lee, Seulgi Lee, Seung Jin Lee, Suhyeon Lee, Yeonjae Lee, Yesol Lee, Youngbeom Lee, Yujin Lee, Shaodong Li, Tianyu Liu, Seong-Eun Moon, Taehong Moon, Max-Lasse Nihlenramstroem, Wonseok Oh, Yuri Oh, Hongbeen Park, Hyekyung Park, Jaeho Park, Nohil Park, Sangjin Park, Jiwon Ryu, Miru Ryu, Simo Ryu, Ahreum Seo, Hee Seo, Kangdeok Seo, Jamin Shin, Seungyoun Shin, Heetae Sin, Jiangping Wang, Lei Wang, Ning Xiang, Longxiang Xiao, Jing Xu, Seonyeong Yi, Haanju Yoo, Haneul Yoo, Hwanhee Yoo, Liang Yu, Youngjae Yu, Weijie Yuan, Bo Zeng, Qian Zhou, Kyunghyun Cho, Jung-Woo Ha, Joonsuk Park, Jihyun Hwang, Hyoung Jo Kwon, Soonyong Kwon, Jungyeon Lee, Seungho Lee, Seonghyeon Lim, Hyunkyung Noh, Seungho Choi, Sang-Woo Lee, Jung Hwa Lim, Nako Sung

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding.

Instruction Following Machine Translation +1

SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation

1 code implementation16 Mar 2024 Uiwon Hwang, Jonghyun Lee, Juhyeon Shin, Sungroh Yoon

We construct an augmentation graph in the feature space of the pretrained model using the neighbor relationships between target features and propose spectral neighborhood clustering to identify partitions in the prediction space.

Data Augmentation Disentanglement +2

Efficient Diffusion-Driven Corruption Editor for Test-Time Adaptation

no code implementations16 Mar 2024 Yeongtak Oh, Jonghyun Lee, Jooyoung Choi, Dahuin Jung, Uiwon Hwang, Sungroh Yoon

To address this, we propose a novel TTA method by leveraging a latent diffusion model (LDM) based image editing model and fine-tuning it with our newly introduced corruption modeling scheme.

Data Augmentation Test-time Adaptation

Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors

1 code implementation12 Mar 2024 Jonghyun Lee, Dahuin Jung, Saehyung Lee, Junsung Park, Juhyeon Shin, Uiwon Hwang, Sungroh Yoon

To mitigate it, TTA methods have utilized the model output's entropy as a confidence metric that aims to determine which samples have a lower likelihood of causing error.

Object Pseudo Label +1

One-Shot Structure-Aware Stylized Image Synthesis

1 code implementation CVPR 2024 Hansam Cho, Jonghyun Lee, Seunggyu Chang, Yonghyun Jeong

While GAN-based models have been successful in image stylization tasks, they often struggle with structure preservation while stylizing a wide range of input images.

Image Generation Image Stylization

Gradient Alignment with Prototype Feature for Fully Test-time Adaptation

no code implementations14 Feb 2024 Juhyeon Shin, Jonghyun Lee, Saehyung Lee, MinJun Park, Dongjun Lee, Uiwon Hwang, Sungroh Yoon

In context of Test-time Adaptation(TTA), we propose a regularizer, dubbed Gradient Alignment with Prototype feature (GAP), which alleviates the inappropriate guidance from entropy minimization loss from misclassified pseudo label.

Pseudo Label Test-time Adaptation

Noise Map Guidance: Inversion with Spatial Context for Real Image Editing

1 code implementation7 Feb 2024 Hansam Cho, Jonghyun Lee, Seoung Bum Kim, Tae-Hyun Oh, Yonghyun Jeong

Text-guided diffusion models have become a popular tool in image synthesis, known for producing high-quality and diverse images.

Image Generation

Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis

1 code implementation17 Jan 2024 Jonghyun Lee, Hansam Cho, Youngjoon Yoo, Seoung Bum Kim, Yonghyun Jeong

Addressing the limitations of text as a source of accurate layout representation in text-conditional diffusion models, many works incorporate additional signals to condition certain attributes within a generated image.

Disentanglement Image Generation

Subsurface Characterization using Ensemble-based Approaches with Deep Generative Models

1 code implementation2 Oct 2023 Jichao Bao, Hongkyu Yoon, Jonghyun Lee

WGAN-GP is trained to generate high-dimensional K fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements.

Dimensionality Reduction Generative Adversarial Network +1

Confidence Score for Source-Free Unsupervised Domain Adaptation

1 code implementation14 Jun 2022 Jonghyun Lee, Dahuin Jung, Junho Yim, Sungroh Yoon

Unlike existing confidence scores that use only one of the source or target domain knowledge, the JMDS score uses both knowledge.

Unsupervised Domain Adaptation

Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry

1 code implementation23 Nov 2021 Mojtaba Forghani, Yizhou Qian, Jonghyun Lee, Matthew Farthing, Tyler Hesser, Peter K. Kitanidis, Eric F. Darve

Here, we propose a reduced-order model (ROM) based approach that utilizes a variational autoencoder (VAE), a type of deep neural network with a narrow layer in the middle, to compress bathymetry and flow velocity information and accelerate bathymetry inverse problems from flow velocity measurements.

Management Uncertainty Quantification

Deep learning-based fast solver of the shallow water equations

no code implementations23 Nov 2021 Mojtaba Forghani, Yizhou Qian, Jonghyun Lee, Matthew W. Farthing, Tyler Hesser, Peter K. Kitanidis, Eric F. Darve

Furthermore, we augment the bathymetry posterior distribution to a more general class of distributions before providing them as inputs to ML algorithm in the second stage.

Management

Confidence Score Weighting Adaptation for Source-Free Unsupervised Domain Adaptation

no code implementations29 Sep 2021 Jonghyun Lee, Dahuin Jung, Junho Yim, Sungroh Yoon

Unsupervised domain adaptation (UDA) aims to achieve high performance within the unlabeled target domain by leveraging the labeled source domain.

Pseudo Label Unsupervised Domain Adaptation

6MapNet: Representing soccer players from tracking data by a triplet network

no code implementations10 Sep 2021 Hyunsung Kim, Jihun Kim, Dongwook Chung, Jonghyun Lee, Jinsung Yoon, Sang-Ki Ko

Although the values of individual soccer players have become astronomical, subjective judgments still play a big part in the player analysis.

A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks

1 code implementation27 May 2021 Teeratorn Kadeethum, Daniel O'Malley, Jan Niklas Fuhg, Youngsoo Choi, Jonghyun Lee, Hari S. Viswanathan, Nikolaos Bouklas

This work is the first to employ and adapt the image-to-image translation concept based on conditional generative adversarial networks (cGAN) towards learning a forward and an inverse solution operator of partial differential equations (PDEs).

Computational Efficiency Image-to-Image Translation +1

Removing Undesirable Feature Contributions Using Out-of-Distribution Data

1 code implementation ICLR 2021 Saehyung Lee, Changhwa Park, Hyungyu Lee, Jihun Yi, Jonghyun Lee, Sungroh Yoon

Herein, we propose a data augmentation method to improve generalization in both adversarial and standard learning by using out-of-distribution (OOD) data that are devoid of the abovementioned issues.

Data Augmentation

Application of deep learning to large scale riverine flow velocity estimation

1 code implementation4 Dec 2020 Mojtaba Forghani, Yizhou Qian, Jonghyun Lee, Matthew W. Farthing, Tyler Hesser, Peter K. Kitanidis, Eric F. Darve

First, using the principal component geostatistical approach (PCGA) we estimate the probability density function of the bathymetry from flow velocity measurements, and then we use multiple machine learning algorithms to obtain a fast solver of the SWEs, given augmented realizations from the posterior bathymetry distribution and the prescribed range of BCs.

Management

Fast and Scalable Earth Texture Synthesis using Spatially Assembled Generative Adversarial Neural Networks

no code implementations13 Nov 2020 Sung Eun Kim, Hongkyu Yoon, Jonghyun Lee

The earth texture with complex morphological geometry and compositions such as shale and carbonate rocks, is typically characterized with sparse field samples because of an expensive and time-consuming characterization process.

Computational Efficiency Texture Synthesis

Joint Contrastive Learning for Unsupervised Domain Adaptation

1 code implementation18 Jun 2020 Changhwa Park, Jonghyun Lee, Jaeyoon Yoo, Minhoe Hur, Sungroh Yoon

Enhancing feature transferability by matching marginal distributions has led to improvements in domain adaptation, although this is at the expense of feature discrimination.

Contrastive Learning Unsupervised Domain Adaptation

Connectivity-informed Drainage Network Generation using Deep Convolution Generative Adversarial Networks

no code implementations16 Jun 2020 Sung Eun Kim, Yongwon Seo, Junshik Hwang, Hongkyu Yoon, Jonghyun Lee

Stochastic network modeling is often limited by high computational costs to generate a large number of networks enough for meaningful statistical evaluation.

PBBFMM3D: a parallel black-box algorithm for kernel matrix-vector multiplication

3 code implementations6 Mar 2019 Ruoxi Wang, Chao Chen, Jonghyun Lee, Eric Darve

We introduce a parallel method that provably requires $O(N)$ operations to reduce the computation cost.

Mathematical Software

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