Search Results for author: Youngmin Kim

Found 8 papers, 1 papers with code

WikiHan: A New Comparative Dataset for Chinese Languages

2 code implementations COLING 2022 Kalvin Chang, Chenxuan Cui, Youngmin Kim, David R. Mortensen

Most comparative datasets of Chinese varieties are not digital; however, Wiktionary includes a wealth of transcriptions of words from these varieties.

ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs

no code implementations12 Mar 2024 Preetam Prabhu Srikar Dammu, Himanshu Naidu, Mouly Dewan, Youngmin Kim, Tanya Roosta, Aman Chadha, Chirag Shah

In the midst of widespread misinformation and disinformation through social media and the proliferation of AI-generated texts, it has become increasingly difficult for people to validate and trust information they encounter.

Fact Checking Knowledge Graphs +1

Automatic Question-Answer Generation for Long-Tail Knowledge

no code implementations3 Mar 2024 Rohan Kumar, Youngmin Kim, Sunitha Ravi, Haitian Sun, Christos Faloutsos, Ruslan Salakhutdinov, Minji Yoon

Pretrained Large Language Models (LLMs) have gained significant attention for addressing open-domain Question Answering (QA).

Answer Generation Knowledge Graphs +2

Keypoint based Sign Language Translation without Glosses

no code implementations22 Apr 2022 Youngmin Kim, Minji Kwak, Dain Lee, Yeongeun Kim, Hyeongboo Baek

However, the SLR is a study that recognizes the unique grammar of sign language, which is different from the spoken language and has a problem that non-disabled people cannot easily interpret.

Sign Language Recognition Sign Language Translation +1

Are Evolutionary Algorithms Safe Optimizers?

no code implementations24 Mar 2022 Youngmin Kim, Richard Allmendinger, Manuel López-Ibáñez

We consider a type of constrained optimization problem, where the violation of a constraint leads to an irrevocable loss, such as breakage of a valuable experimental resource/platform or loss of human life.

Evolutionary Algorithms

Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art

no code implementations23 Jan 2021 Youngmin Kim, Richard Allmendinger, Manuel López-Ibáñez

Safe learning and optimization deals with learning and optimization problems that avoid, as much as possible, the evaluation of non-safe input points, which are solutions, policies, or strategies that cause an irrecoverable loss (e. g., breakage of a machine or equipment, or life threat).

Active Learning Evolutionary Algorithms +3

Data Synthesis based on Generative Adversarial Networks

no code implementations9 Jun 2018 Noseong Park, Mahmoud Mohammadi, Kshitij Gorde, Sushil Jajodia, Hongkyu Park, Youngmin Kim

We call this property model compatibility.

Databases Cryptography and Security H.3.4; I.2; K.6.5

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