15 citations · 21 across the 11 of their papers we have counts for
11 papers
PLC-DPO: Posterior Label Correction in Noisy and Ambiguous Preference Optimization
Boryeong Cho, Sumyeong Ahn, Se-Young Yun
Direct Preference Optimization (DPO) simplifies alignment through pairwise comparisons but assumes all observed preferences are reliable. Real data often violates this assumption,…
FOAM: Frequency and Operator Error-Based Adaptive Damping Method for Reducing Staleness-Oriented Error for Shampoo
Kyunghun Nam, Sumyeong Ahn
Shampoo is attracting considerable attention for its superior performance on large-scale optimization benchmarks; yet it faces a significant practical bottleneck: the prohibitive c…
Stage-wise Attention-Guided Region Sequencing for Adversarial Attacks on Large Vision-Language Models
Jaehyun Kwak, Nam Cao, Boryeong Cho +3
Targeted adversarial attacks on Large Vision-Language Models (LVLMs) test whether small image perturbations can steer model responses toward attacker-specified content. Under the s…
VACoDe: Visual Augmented Contrastive Decoding
Sihyeon Kim, Boryeong Cho, Sangmin Bae +2
Despite the astonishing performance of recent Large Vision-Language Models (LVLMs), these models often generate inaccurate responses. To address this issue, previous studies have f…
FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning
Seongyoon Kim, Minchan Jeong, Sungnyun Kim +3
Federated Learning (FL) has emerged as a pivotal framework for the development of effective global models (global FL) or personalized models (personalized FL) across clients with h…
Fine tuning Pre trained Models for Robustness Under Noisy Labels
Sumyeong Ahn, Sihyeon Kim, Jongwoo Ko +1
The presence of noisy labels in a training dataset can significantly impact the performance of machine learning models. To tackle this issue, researchers have explored methods for…
NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models
Jongwoo Ko, Seungjoon Park, Yujin Kim +4
Structured pruning methods have proven effective in reducing the model size and accelerating inference speed in various network architectures such as Transformers. Despite the vers…
CUDA: Curriculum of Data Augmentation for Long-Tailed Recognition
Sumyeong Ahn, Jongwoo Ko, Se-Young Yun
Class imbalance problems frequently occur in real-world tasks, and conventional deep learning algorithms are well known for performance degradation on imbalanced training datasets.…
Denoising after Entropy-based Debiasing A Robust Training Method for Dataset Bias with Noisy Labels
Sumyeong Ahn, Se-Young Yun
Improperly constructed datasets can result in inaccurate inferences. For instance, models trained on biased datasets perform poorly in terms of generalization (i.e., dataset bias).…
Mitigating Dataset Bias by Using Per-sample Gradient
Sumyeong Ahn, Seongyoon Kim, Se-young Yun
The performance of deep neural networks is strongly influenced by the training dataset setup. In particular, when attributes having a strong correlation with the target attribute a…
Enlarging Discriminative Power by Adding an Extra Class in Unsupervised Domain Adaptation
Hai H. Tran, Sumyeong Ahn, Taeyoung Lee +1
In this paper, we study the problem of unsupervised domain adaptation that aims at obtaining a prediction model for the target domain using labeled data from the source domain and…