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Eunchung Noh
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2020 – today
- 2025
- [c5]Andrea Maracani, Savas Özkan, Sijun Cho, Hyowon Kim, Eunchung Noh, Jeongwon Min, Cho Jung Min, Dookun Park, Mete Ozay:
Accurate Scene Text Recognition with Efficient Model Scaling and Cloze Self-Distillation. CVPR 2025: 14516-14526 - [c4]Savas Özkan, Taha Ceritli, Jeongwon Min, Eunchung Noh, Jung Min Cho, Dookun Park, Mete Ozay:
A Study of Improving The Privacy-Utility Trade-off of Task-specific Models with Learnable Privacy. ICASSP 2025: 1-5 - [c3]Savas Özkan, Taha Ceritli, Jeongwon Min, Eunchung Noh, Jung Min Cho, Dookun Park, Mete Ozay:
Hyper-Refinement for Low-Rank Adaptation. ICASSP 2025: 1-5 - [c2]Savas Özkan
, Andrea Maracani
, Mete Ozay, Hyowon Kim
, Sijun Cho
, Eunchung Noh
, Jeongwon Min
, Jung Min Cho
:
Efficient and Accurate Scene Text Recognition with Cascaded-Transformers. MMSys 2025: 335-341 - [i5]Eunchung Noh, Jeonghun Baek:
Toward Responsible Federated Large Language Models: Leveraging a Safety Filter and Constitutional AI. CoRR abs/2502.16691 (2025) - [i4]Andrea Maracani, Savas Özkan, Sijun Cho, Hyowon Kim, Eunchung Noh, Jeongwon Min, Cho Jung Min, Dookun Park, Mete Ozay:
Accurate Scene Text Recognition with Efficient Model Scaling and Cloze Self-Distillation. CoRR abs/2503.16184 (2025) - [i3]Savas Özkan, Andrea Maracani, Hyowon Kim, Sijun Cho, Eunchung Noh, Jeongwon Min, Jung Min Cho, Mete Ozay:
Efficient and Accurate Scene Text Recognition with Cascaded-Transformers. CoRR abs/2503.18883 (2025) - [i2]Junyi Zhu, Ruicong Yao, Taha Ceritli, Savas Özkan, Matthew B. Blaschko, Eunchung Noh, Jeongwon Min, Cho Jung Min, Mete Ozay:
Unlocking the Value of Decentralized Data: A Federated Dual Learning Approach for Model Aggregation. CoRR abs/2503.20138 (2025) - [i1]You Rim Choi, Subeom Park, Seojun Heo, Eunchung Noh, Hyung-Sin Kim:
The Others: Naturally Isolating Out-of-Distribution Samples for Robust Open-Set Semi-Supervised Learning. CoRR abs/2504.12569 (2025) - 2024
- [c1]Taha Ceritli, Savas Özkan, Jeongwon Min, Eunchung Noh, Cho Min, Mete Ozay:
A Study of Parameter Efficient Fine-tuning by Learning to Efficiently Fine-Tune. EMNLP (Findings) 2024: 15819-15836
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last updated on 2025-07-19 03:12 CEST by the dblp team
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