2 citations · 2 across the 5 of their papers we have counts for
5 papers
Don't Lose Entities from Retrieval to Generation: Dual Entity Recovery RAG for multi-hop QA
Heechang Lee, Dong-Young Lim
Retrieval-augmented multi-hop question answering (QA) decomposes a query into sub-questions and decomposes the corpus into smaller retrieval units such as sentences. Both forms of…
Controllable Machine Unlearning via Gradient Pivoting
Youngsik Hwang, Dong-Young Lim
Machine unlearning (MU) aims to remove the influence of specific data from a trained model. However, approximate unlearning methods, often formulated as a single-objective optimiza…
Flatness-Aware Stochastic Gradient Langevin Dynamics
Stefano Bruno, Youngsik Hwang, Jaehyeon An +2
Flatness of the loss landscape has been widely studied as an important perspective for understanding the behavior and generalization of deep learning algorithms. Motivated by this…
DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization
Youngjun Song, Youngsik Hwang, Jonghun Lee +2
Domain generalization (DG) aims to learn models that perform well on unseen target domains by training on multiple source domains. Sharpness-Aware Minimization (SAM), known for fin…
Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
Youngsik Hwang, Dong-Young Lim
Physics-informed neural networks (PINNs) have emerged as a prominent approach for solving partial differential equations (PDEs) by minimizing a combined loss function that incorpor…