3 citations · 3 across the 6 of their papers we have counts for
6 papers
AQuaUI: Visual Token Reduction for GUI Agents with Adaptive Quadtrees
Yuankai Li, Tinghui Zhu, Ha Min Son +3
Large Multimodal Models (LMMs) have recently emerged as promising backbones for GUI-agent models, where high-resolution GUI screenshots are introduced to the prompts at each iterat…
Automating Android Build Repair: Bridging the Reasoning-Execution Gap in LLM Agents with Domain-Specific Tools
Ha Min Son, Huan Ren, Xin Liu +1
Android is the largest mobile platform, yet automatically building applications remains a practical challenge. While Large Language Models (LLMs) show promise for code repair, thei…
Domain Generalization in-the-Wild: Disentangling Classification from Domain-Aware Representations
Ha Min Son, Zhe Zhao, Shahbaz Rezaei +1
Evaluating domain generalization (DG) for foundational models like CLIP is challenging, as web-scale pretraining data potentially covers many existing benchmarks. Consequently, cur…
FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization
Ha Min Son, Shahbaz Rezaei, Xin Liu
Semi-supervised domain generalization (SSDG) aims to solve the problem of generalizing to out-of-distribution data when only a few labels are available. Due to label scarcity, appl…
FedUV: Uniformity and Variance for Heterogeneous Federated Learning
Ha Min Son, Moon-Hyun Kim, Tai-Myoung Chung +2
Federated learning is a promising framework to train neural networks with widely distributed data. However, performance degrades heavily with heterogeneously distributed data. Rece…
Compare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data
Ha Min Son, Moon Hyun Kim, Tai-Myoung Chung
Federated Learning is a widely adopted method to train neural networks over distributed data. One main limitation is the performance degradation that occurs when data is heterogene…