선도 연구 주제

전체 논문

NAIRL이 세 가지 선도 연구 주제에 걸쳐 발표한 연구의 전체 색인입니다.

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전체 논문

논문 174개
# Foundation Model for Robotics

Quantized Factor Identifiable Causal Effect Variational Autoencoder

Sujeong Song, Junghyo Sohn, Eunsong Kang, Heung-Il Suk · CIKM · 2025
# Foundation Model for Robotics

Frequency-Conditioned Diffusion Models for Time Series Generation

Seungwoo Jeong, Junghyo Sohn, Jaehyun Jeon, Heung-Il Suk · CIKM · 2025
# Foundation Model for Robotics

ExpertDiff: Head-less Model Reprogramming with Diffusion Classifiers for Out-of-Distribution Generalization

Jee Seok Yoon, Junghyo Sohn, Wootaek Jeong, Heung-Il Suk · IJCAI · 2025
# Foundation Model for Robotics

Connecting the Knowledge Dots: Retrieval-augmented Knowledge Connection for Commonsense Reasoning

Junho Kim, Soyeon Bak, Mingyu Lee, Minju Hong, Songha Kim, Tae-Eui Kam, SangKeun Lee · EMNLP · 2025
# Foundation Model for Robotics

Bridging the Gap Between Molecule and Textual Descriptions via Substructure-aware Alignment

Hyuntae Park, Yeachan Kim, SangKeun Lee · EMNLP · 2025
# Foundation Model for Robotics

“Going to a trap house” conveys more fear than “Going to a mall”: Benchmarking Emotion Context Sensitivity for LLMs

Eojin Jeon, Mingyu Lee, Sangyun Kim, Junho Kim, Wanzee Cho, Tae-Eui Kam, SangKeun Lee · EMNLP · 2025
# Foundation Model for Robotics

Polishing Every Facet of the GEM: Testing Linguistic Competence of LLMs and Humans in Korean

SungHo Kim, Nayeon Kim, Taehee Jeon, SangKeun Lee · ACL · 2025
# Foundation Model for Robotics

Curriculum Debiasing: Toward Robust Parameter-Efficient Fine-Tuning Against Dataset Biases

Mingyu Lee, Yeachan Kim, Wing-Lam Mok, SangKeun Lee · ACL · 2025
# Foundation Model for Robotics

Forward Knows Efficient Backward Path: Saliency-Guided Memory-Efficient Fine-tuning of Large Language Models

Yeachan Kim, SangKeun Lee · ACL · 2025
# Foundation Model for Robotics

Incorporating Domain Knowledge into Materials Tokenization

Yerim Oh, Jun-Hyung Park, Junho Kim, SungHo Kim, SangKeun Lee · ACL · 2025