no code implementations • 13 Nov 2023 • Jin Myung Kwak, Minseon Kim, Sung Ju Hwang
Recent language models have achieved impressive performance in natural language tasks by incorporating instructions with task input during fine-tuning.
1 code implementation • 26 May 2023 • Hayeon Lee, Sohyun An, Minseon Kim, Sung Ju Hwang
Previous DaNAS methods have mostly tackled the search for the neural architecture for fixed datasets and the teacher, which are not generalized well on a new task consisting of an unseen dataset and an unseen teacher, thus need to perform a costly search for any new combination of the datasets and the teachers.
no code implementations • 6 Apr 2023 • Seungpyo Kang, Minseon Kim, Jiwon Sun, Myeonghun Lee, Kyoungmin Min
Protein aggregation occurs when misfolded or unfolded proteins physically bind together, and can promote the development of various amyloid diseases.
1 code implementation • 19 Oct 2022 • Jin Myung Kwak, Minseon Kim, Sung Ju Hwang
Transformer-based Language Models (LMs) have achieved impressive results on natural language understanding tasks, but they can also generate toxic text such as insults, threats, and profanity, limiting their real-world applications.
no code implementations • 19 Oct 2022 • Minseon Kim, Hyeonjeong Ha, Dong Bok Lee, Sung Ju Hwang
Despite the success on few-shot learning problems, most meta-learned models only focus on achieving good performance on clean examples and thus easily break down when given adversarially perturbed samples.
no code implementations • 4 May 2022 • Minjong Cheon, Minseon Kim, Hanseon Joo
The Korean wave, which denotes the global popularity of South Korea's cultural economy, contributes to the increasing demand for the Korean language.
no code implementations • 14 Feb 2022 • Seungpyo Kang, Minseon Kim, Kyoungmin Min
In this study, a platform consisting of a high-throughput screening and a machine-learning surrogate model for discovering superionic Li-SSEs among 20, 237 Li-containing materials is developed.
no code implementations • ICML Workshop AML 2021 • Minseon Kim, Jihoon Tack, Jinwoo Shin, Sung Ju Hwang
Adversarial training methods, which minimizes the loss of adversarially-perturbed training examples, have been extensively studied as a solution to improve the robustness of the deep neural networks.
1 code implementation • ICML Workshop AML 2021 • Jihoon Tack, Sihyun Yu, Jongheon Jeong, Minseon Kim, Sung Ju Hwang, Jinwoo Shin
Adversarial training (AT) is currently one of the most successful methods to obtain the adversarial robustness of deep neural networks.
2 code implementations • NeurIPS 2020 • Minseon Kim, Jihoon Tack, Sung Ju Hwang
In this paper, we propose a novel adversarial attack for unlabeled data, which makes the model confuse the instance-level identities of the perturbed data samples.
1 code implementation • 22 Aug 2019 • Deokyun Kim, Minseon Kim, Gihyun Kwon, Dae-shik Kim
Face Super-Resolution (SR) is a subfield of the SR domain that specifically targets the reconstruction of face images.
Ranked #1 on Face Alignment on CelebA + AFLW Unaligned