Search Results for author: Jooyoung Lee

Found 16 papers, 8 papers with code

Molecular mechanism of anion permeation through aquaporin 6

no code implementations8 Nov 2023 Eiji Yamamoto, Keehyoung Joo, Jooyoung Lee, Mark S. P. Sansom, Masato Yasui

Moreover, we demonstrate the pH-sensing mechanism in which the protonation of H184 and H189 under low pH conditions allosterically triggers the gating of the SF region.

Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation

1 code implementation24 Oct 2023 Jason Lucas, Adaku Uchendu, Michiharu Yamashita, Jooyoung Lee, Shaurya Rohatgi, Dongwon Lee

Recent ubiquity and disruptive impacts of large language models (LLMs) have raised concerns about their potential to be misused (. i. e, generating large-scale harmful and misleading content).

COMPASS: High-Efficiency Deep Image Compression with Arbitrary-scale Spatial Scalability

no code implementations ICCV 2023 Jongmin Park, Jooyoung Lee, Munchurl Kim

Recently, neural network (NN)-based image compression studies have actively been made and has shown impressive performance in comparison to traditional methods.

Image Compression

Comparison of L2 Korean pronunciation error patterns from five L1 backgrounds by using automatic phonetic transcription

1 code implementation19 Jun 2023 Eun Jung Yeo, Hyungshin Ryu, Jooyoung Lee, Sunhee Kim, Minhwa Chung

This paper presents a large-scale analysis of L2 Korean pronunciation error patterns from five different language backgrounds, Chinese, Vietnamese, Japanese, Thai, and English, by using automatic phonetic transcription.

Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?

2 code implementations3 Apr 2023 Adaku Uchendu, Jooyoung Lee, Hua Shen, Thai Le, Ting-Hao 'Kenneth' Huang, Dongwon Lee

Advances in Large Language Models (e. g., GPT-4, LLaMA) have improved the generation of coherent sentences resembling human writing on a large scale, resulting in the creation of so-called deepfake texts.

Face Swapping Human Detection +1

Selective compression learning of latent representations for variable-rate image compression

1 code implementation8 Nov 2022 Jooyoung Lee, Seyoon Jeong, Munchurl Kim

For this, we first generate a 3D importance map as the nature of input content to represent the underlying importance of the representation elements.

Image Compression

Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense

1 code implementation Findings (ACL) 2022 Thai Le, Jooyoung Lee, Kevin Yen, Yifan Hu, Dongwon Lee

We find that adversarial texts generated by ANTHRO achieve the best trade-off between (1) attack success rate, (2) semantic preservation of the original text, and (3) stealthiness--i. e. indistinguishable from human writings hence harder to be flagged as suspicious.

Adversarial Attack

Do Language Models Plagiarize?

1 code implementation15 Mar 2022 Jooyoung Lee, Thai Le, Jinghui Chen, Dongwon Lee

Our results suggest that (1) three types of plagiarism widely exist in LMs beyond memorization, (2) both size and decoding methods of LMs are strongly associated with the degrees of plagiarism they exhibit, and (3) fine-tuned LMs' plagiarism patterns vary based on their corpus similarity and homogeneity.

Language Modelling Memorization +1

An End-to-End Joint Learning Scheme of Image Compression and Quality Enhancement with Improved Entropy Minimization

1 code implementation30 Dec 2019 Jooyoung Lee, Seunghyun Cho, Munchurl Kim

In order to show the effectiveness of our proposed JointIQ-Net, extensive experiments have been performed, and showed that the JointIQ-Net achieves a remarkable performance improvement in coding efficiency in terms of both PSNR and MS-SSIM, compared to the previous learned image compression methods and the conventional codecs such as VVC Intra (VTM 7. 1), BPG, and JPEG2000.

Image Compression MS-SSIM +1

Self-adaptive node-based PCA encodings

no code implementations16 Jun 2017 Leonard Johard, Victor Rivera, Manuel Mazzara, Jooyoung Lee

In this paper we propose an algorithm, Simple Hebbian PCA, and prove that it is able to calculate the principal component analysis (PCA) in a distributed fashion across nodes.

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