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2020 – today
- 2024
[c10]Sungnyun Kim, Haofu Liao, Srikar Appalaraju, Peng Tang, Zhuowen Tu, Ravi Kumar Satzoda, R. Manmatha, Vijay Mahadevan, Stefano Soatto:
DocKD: Knowledge Distillation from LLMs for Open-World Document Understanding Models. EMNLP 2024: 3167-3193- [c9]Kangwook Jang, Sungnyun Kim, Hoirin Kim:
STaR: Distilling Speech Temporal Relation for Lightweight Speech Self-Supervised Learning Models. ICASSP 2024: 10721-10725 - [c8]Jongwoo Ko, Sungnyun Kim, Tianyi Chen, Se-Young Yun:
DistiLLM: Towards Streamlined Distillation for Large Language Models. ICML 2024 - [i15]Jongwoo Ko, Sungnyun Kim, Tianyi Chen, Se-Young Yun:
DistiLLM: Towards Streamlined Distillation for Large Language Models. CoRR abs/2402.03898 (2024) - [i14]Seongyoon Kim, Minchan Jeong, Sungnyun Kim, Sungwoo Cho, Sumyeong Ahn, Se-Young Yun:
FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning. CoRR abs/2406.02355 (2024) - [i13]Sungnyun Kim, Kangwook Jang, Sangmin Bae, Hoirin Kim, Se-Young Yun:
Learning Video Temporal Dynamics with Cross-Modal Attention for Robust Audio-Visual Speech Recognition. CoRR abs/2407.03563 (2024) - [i12]Jihwan Oh, Sungnyun Kim, Gahee Kim, Sunghwan Kim, Se-Young Yun:
Diffusion-based Episodes Augmentation for Offline Multi-Agent Reinforcement Learning. CoRR abs/2408.13092 (2024) - [i11]Sungnyun Kim, Haofu Liao, Srikar Appalaraju, Peng Tang, Zhuowen Tu, Ravi Kumar Satzoda, R. Manmatha, Vijay Mahadevan, Stefano Soatto:
DocKD: Knowledge Distillation from LLMs for Open-World Document Understanding Models. CoRR abs/2410.03061 (2024) - 2023
- [c7]Sangmin Bae, Sungnyun Kim, Jongwoo Ko, Gihun Lee, Seungjong Noh, Se-Young Yun:
Self-Contrastive Learning: Single-Viewed Supervised Contrastive Framework Using Sub-network. AAAI 2023: 197-205 - [c6]Sungnyun Kim, Sangmin Bae, Se-Young Yun:
Coreset Sampling from Open-Set for Fine-Grained Self-Supervised Learning. CVPR 2023: 7537-7547 - [c5]Kangwook Jang, Sungnyun Kim, Se-Young Yun, Hoirin Kim:
Recycle-and-Distill: Universal Compression Strategy for Transformer-based Speech SSL Models with Attention Map Reusing and Masking Distillation. INTERSPEECH 2023: 316-320 - [c4]Sangmin Bae, June-Woo Kim, Won-Yang Cho, Hyerim Baek, Soyoun Son, Byungjo Lee, Changwan Ha, Kyongpil Tae, Sungnyun Kim, Se-Young Yun:
Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification. INTERSPEECH 2023: 5436-5440 - [i10]Sungnyun Kim, Sangmin Bae, Se-Young Yun:
Coreset Sampling from Open-Set for Fine-Grained Self-Supervised Learning. CoRR abs/2303.11101 (2023) - [i9]Kangwook Jang, Sungnyun Kim, Se-Young Yun, Hoirin Kim:
Recycle-and-Distill: Universal Compression Strategy for Transformer-based Speech SSL Models with Attention Map Reusing and Masking Distillation. CoRR abs/2305.11685 (2023) - [i8]Sangmin Bae, June-Woo Kim, Won-Yang Cho, Hyerim Baek, Soyoun Son, Byungjo Lee, Changwan Ha, Kyongpil Tae, Sungnyun Kim, Se-Young Yun:
Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification. CoRR abs/2305.14032 (2023) - [i7]Sungnyun Kim, Junsoo Lee, Kibeom Hong, Daesik Kim, Namhyuk Ahn:
DiffBlender: Scalable and Composable Multimodal Text-to-Image Diffusion Models. CoRR abs/2305.15194 (2023) - [i6]Kangwook Jang, Sungnyun Kim, Hoirin Kim:
STaR: Distilling Speech Temporal Relation for Lightweight Speech Self-Supervised Learning Models. CoRR abs/2312.09040 (2023) - 2022
- [j1]Sungnyun Kim
, Se-Young Yun:
Calibration of Few-Shot Classification Tasks: Mitigating Misconfidence From Distribution Mismatch. IEEE Access 10: 53894-53908 (2022) - [c3]Jaehoon Oh, Sungnyun Kim, Namgyu Ho, Jin-Hwa Kim, Hwanjun Song, Se-Young Yun:
ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning. CIKM 2022: 4359-4363 - [c2]Sungnyun Kim, Jaewoo Shin, Seongha Eom, Jihwan Oh, Se-Young Yun:
Real-time and Explainable Detection of Epidemics with Global News Data. Healthcare AI and COVID-19 Workshop 2022: 73-90 - [c1]Jaehoon Oh, Sungnyun Kim, Namgyu Ho, Jin-Hwa Kim, Hwanjun Song, Se-Young Yun:
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty. NeurIPS 2022 - [i5]Jaehoon Oh, Sungnyun Kim, Namgyu Ho, Jin-Hwa Kim, Hwanjun Song, Se-Young Yun:
Understanding Cross-Domain Few-Shot Learning: An Experimental Study. CoRR abs/2202.01339 (2022) - [i4]Jaehoon Oh, Sungnyun Kim, Namgyu Ho, Jin-Hwa Kim, Hwanjun Song, Se-Young Yun:
ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning. CoRR abs/2205.05282 (2022) - [i3]Yujin Kim, Jaehoon Oh, Sungnyun Kim, Se-Young Yun:
Revisiting the Updates of a Pre-trained Model for Few-shot Learning. CoRR abs/2205.07874 (2022) - 2021
- [i2]Sangmin Bae, Sungnyun Kim, Jongwoo Ko, Gihun Lee, Seungjong Noh, Se-Young Yun:
Self-Contrastive Learning. CoRR abs/2106.15499 (2021) - 2020
- [i1]Sungnyun Kim, Gihun Lee, Sangmin Bae, Se-Young Yun:
MixCo: Mix-up Contrastive Learning for Visual Representation. CoRR abs/2010.06300 (2020)
Coauthor Index
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