5 papers
Mitigating Resolution-Drift in Federated Learning: Case of Keypoint Detection
Taeheon Lim, Joohyung Lee, Kyungjae Lee +1
The Federated Learning (FL) approach enables effective learning across distributed systems, while preserving user data privacy. To date, research has primarily focused on addressin…
Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environments
Woonsang Kang, Joohyung Lee, Seungjun Kim +2
Grasp pose detection (GPD) is a fundamental capability for robotic autonomy, but its reliance on large, diverse datasets creates significant data privacy and centralization challen…
Pathwise Explanation of ReLU Neural Networks
Seongwoo Lim, Won Jo, Joohyung Lee +1
Neural networks have demonstrated a wide range of successes, but their ``black box" nature raises concerns about transparency and reliability. Previous research on ReLU networks ha…
Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs
Anshumann, Mohd Abbas Zaidi, Akhil Kedia +5
Knowledge distillation can be a cost-effective technique to distill knowledge in Large Language Models, if the teacher output logits can be pre-computed and cached. However, succes…
Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challenge
Joohyun Lee, Minji Roh
As Large Language Models (LLMs) increasingly address domain-specific problems, their application in the financial sector has expanded rapidly. Tasks that are both highly valuable a…