Search Results for author: Young-Min Kim

Found 9 papers, 1 papers with code

Insurance Question Answering via Single-turn Dialogue Modeling

no code implementations CAI (COLING) 2022 Seon-Ok Na, Young-Min Kim, Seung-Hwan Cho

With great success in single-turn question answering (QA), conversational QA is currently receiving considerable attention.

Intent Detection Question Answering +3

Bridging Dynamic Factor Models and Neural Controlled Differential Equations for Nowcasting GDP

2 code implementations13 Sep 2024 Seonkyu Lim, Jeongwhan Choi, Noseong Park, Sang-Ha Yoon, ShinHyuck Kang, Young-Min Kim, Hyunjoong Kang

However, DFMs face two main challenges: i) the lack of capturing economic uncertainties such as sudden recessions or booms, and ii) the limitation of capturing irregular dynamics from mixed-frequency data.

Irregular Time Series

Key-point Guided Deformable Image Manipulation Using Diffusion Model

no code implementations16 Jan 2024 Seok-Hwan Oh, Guil Jung, Myeong-Gee Kim, Sang-Yun Kim, Young-Min Kim, Hyeon-Jik Lee, Hyuk-Sool Kwon, Hyeon-Min Bae

In this paper, we introduce a Key-point-guided Diffusion probabilistic Model (KDM) that gains precise control over images by manipulating the object's key-point.

Image Manipulation Optical Flow Estimation +1

Image-Object-Specific Prompt Learning for Few-Shot Class-Incremental Learning

no code implementations6 Sep 2023 In-Ug Yoon, Tae-Min Choi, Sun-Kyung Lee, Young-Min Kim, Jong-Hwan Kim

To create these IOS classifiers, we encode a bias prompt into the classifiers using our specially designed module, which harnesses key-prompt pairs to pinpoint the IOS features of classes in each session.

class-incremental learning Few-Shot Class-Incremental Learning +1

Balanced Supervised Contrastive Learning for Few-Shot Class-Incremental Learning

no code implementations26 May 2023 In-Ug Yoon, Tae-Min Choi, Young-Min Kim, Jong-Hwan Kim

Few-shot class-incremental learning (FSCIL) presents the primary challenge of balancing underfitting to a new session's task and forgetting the tasks from previous sessions.

class-incremental learning Contrastive Learning +2

Investigating the Image of Entities in Social Media: Dataset Design and First Results

no code implementations LREC 2014 Julien Velcin, Young-Min Kim, Caroline Brun, Jean-Yves Dormagen, Eric SanJuan, Leila Khouas, Anne Peradotto, Stephane Bonnevay, Claude Roux, Julien Boyadjian, Alej Molina, ro, Marie Neihouser

The objective of this paper is to describe the design of a dataset that deals with the image (i. e., representation, web reputation) of various entities populating the Internet: politicians, celebrities, companies, brands etc.

Clustering Information Retrieval +2

Annotated Bibliographical Reference Corpora in Digital Humanities

no code implementations LREC 2012 Young-Min Kim, Patrice Bellot, Elodie Faath, Marin Dacos

In this paper, we present new bibliographical reference corpora in digital humanities (DH) that have been developed under a research project, Robust and Language Independent Machine Learning Approaches for Automatic Annotation of Bibliographical References in DH Books supported by Google Digital Humanities Research Awards.

BIG-bench Machine Learning

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