Publications_1 - National AI Research Lab

#Breakthrough in Neural Scaling Law

Rethinking Shapley Value for Negative Interactions in Non-convex Games

Year: 2025 / Author: Wonjoon Chang, Myeongjin Lee, Jaesik Choi / Conference: International Conference on Learning Representations (ICLR)

#Breakthrough in Neural Scaling Law

Granular Concept Circuits: Toward a Fine-Grained Circuit Discovery for Concept Representations

Year: 2025 / Author: Dahee Kwon, Sehyun Lee, Jaesik Choi / Conference: IEEE/CVF International Conference on Computer Vision (ICCV)

#Breakthrough in Neural Scaling Law

Enhancing Creative Generation on Stable Diffusion-based Models

Year: 2025 / Author: Jiyeon Han, Dahee Kwon, Gayoung Lee, Junho Kim, Jaesik Choi / Conference: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

#Breakthrough in Neural Scaling Law

Local Manifold Approximation and Projection for Manifold-Aware Diffusion Planning

Year: 2025 / Author: Kyowoon Lee, Jaesik Choi / Conference: International Conference on Machine Learning (ICML)

#Breakthrough in Neural Scaling Law

State-Covering Trajectory Stitching for Diffusion Planners

Year: 2025 / Author: Kyowoon Lee, Jaesik Choi / Conference: Conference on Neural Information Processing Systems (NeurIPS)

#Breakthrough in Neural Scaling Law

Learning Advanced Self-Attention for Linear Transformers in the Singular Value Domain

Year: 2025 / Author: Hyowon Wi, Jeongwhan Choi, Noseong Park / Conference: International Joint Conference on Artificial Intelligence (IJCAI)

#Breakthrough in Neural Scaling Law

Neural Functions for Learning Periodic Signal

Year: 2025 / Author: Woojin Cho, Minju Jo, Kookjin Lee, Noseong Park / Conference: International Conference on Learning Representations (ICLR)

#Breakthrough in Neural Scaling Law

SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation

Year: 2025 / Author: Chaejeong Lee, Jeongwhan Choi, Hyowon Wi, Sung-Bae Cho, Noseong Park / Conference: ACM International Conference on Web Search and Data Mining (WSDM)

#Breakthrough in Neural Scaling Law

Debunking the CUDA Myth Towards GPU-based AI Systems: Evaluation of the Performance and Programmability of Intel’s Gaudi NPU for AI Model Serving

Year: 2025 / Author: Yunjae Lee, Juntaek Lim, Jehyeon Bang, Eunyeong Cho, Huijong Jeong, Taesu Kim, Hyungjun Kim, Joonhyung Lee, Jinseop Im, Ranggi Hwang, Se Jung Kwon, Dongsoo Lee, Minsoo Rhu / Conference: International Symposium on Computer Architecture (ISCA)

#Breakthrough in Neural Scaling Law

Mamba-X: An End-to-End Vision Mamba Accelerator for Edge Computing Devices

Year: 2025 / Author: Dongho Yoon, Gungyu Lee, Jaewon Chang, Yunjae Lee, Dongjae Lee, Minsoo Rhu / Conference: International Conference on Computer-Aided Design (ICCAD)

#Breakthrough in Neural Scaling Law

The Cost of Robustness: Tighter Bounds on Parameter Complexity for Robust Memorization in ReLU Nets

Year: 2025 / Author: Yujun Kim, Chaewon Moon, Chulhee Yun / Conference: Conference on Neural Information Processing Systems (NeurIPS)

#Breakthrough in Neural Scaling Law

Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty

Year: 2025 / Author: Yeseul Cho, Baekrok Shin, Changmin Kang, Chulhee Yun / Conference: International Conference on Machine Learning (ICML)

#Breakthrough in Neural Scaling Law

Self-Training Elicits Concise Reasoning in Large Language Models

Year: 2025 / Author: Tergel Munkhbat, Namgyu Ho, Seo Hyun Kim, Yongjin Yang, Yujin Kim, Se-Young Yun / Paper: Annual Meeting of the Association for Computational Linguistics (ACL)

#Breakthrough in Neural Scaling Law

Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation

Year: 2025 / Author: Sangmin Bae, Yujin Kim, Reza Bayat, Sungnyun Kim, Jiyoun Ha, Tal Schuster, Adam Fisch, Hrayr Harutyunyan, Ziwei Ji, Aaron Courville, Se-Young Yun / Conference: Conference on Neural Information Processing Systems (NeurIPS)

#Breakthrough in Neural Scaling Law

DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMs

Year: 2025 / Author: Jongwoo Ko, Tianyi Chen, Sungnyun Kim, Tianyu Ding, Luming Liang, Ilya Zharkov, Se-Young Yun / Conference: International Conference on Machine Learning (ICML)

#Breakthrough in Neural Scaling Law

Beyond Neighbors: Distance-Generalized Graphlets for Enhanced Graph Characterization

Year: 2025 / Author: Yeongho Kim, Yuyeong Kim, Geon Lee, Kijung Shin / Conference: The ACM Web Conference (WWW)

#Breakthrough in Neural Scaling Law

Learning to Flow from Generative Pretext Tasks for Neural Architecture Encoding

Year: 2025 / Author: Sunwoo Kim, Hyunjin Hwang, Kijung Shin / Paper: Conference on Neural Information Processing Systems (NeurIPS)

#Breakthrough in Neural Scaling Law

‘Hello, World!’: Making GNNs Talk with LLMs

Year: 2025 / Author: Sunwoo Kim, Soo Yong Lee, Jaemin Yoo, Kijung Shin / Paper: Conference on Empirical Methods in Natural Language Processing (EMNLP)

#Breakthrough in Neural Scaling Law

LANTERN: Accelerating Visual Autoregressive Models with Relaxed Speculative Decoding

Year: 2025 / Author: Doohyuk Jang, Sihwan Park, June Yong Yang, Yeonsung Jung, Jihun Yun, Souvik Kundu, Sung-Yub Kim, Eunho Yang / Conference: International Conference on Learning Representations (ICLR)

#Breakthrough in Neural Scaling Law

Divide and Translate: Compositional First-Order Logic Translation and Verification for Complex Logical Reasoning

Year: 2025 / Author: Hyun Ryu, Gyeongman Kim, Hyemin S. Lee, Eunho Yang / Conference: International Conference on Learning Representations (ICLR)

Publications