Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation T Kim, J Oh, NY Kim, S Cho, SY Yun IJCAI 2021, 2021 | 141 | 2021 |
FINE Samples for Learning with Noisy Labels T Kim, J Ko, J Choi, S Cho, SY Yun NeurIPS 2021, 2021 | 86 | 2021 |
A Survey of Supernet Optimization and its Applications: Spatial and Temporal Optimization for Neural Architecture Search S Cha, T Kim, H Lee, SY Yun arXiv preprint arXiv:2204.03916, 2022 | 13* | 2022 |
Revisiting orthogonality regularization: a study for convolutional neural networks in image classification T Kim, SY Yun IEEE Access 10, 69741-69749, 2022 | 9 | 2022 |
Accurate and fast federated learning via combinatorial multi-armed bandits T Kim, S Bae, J Lee, S Yun arXiv, 2020 | 9 | 2020 |
Benchmark Dataset for Precipitation Forecasting by Post-Processing the Numerical Weather Prediction T Kim, N Ho, D Kim, SY Yun arXiv, 2022 | 6 | 2022 |
Weather4cast at NeurIPS 2022: Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts A Gruca, F Serva, L Lliso, P Rípodas, X Calbet, P Herruzo, J Pihrt, ... NeurIPS 2022 Competition Track, 292-313, 2023 | 5 | 2023 |
Supernet Training for Federated Image Classification under System Heterogeneity T Kim, SY Yun ICML 2022 Workshop: Dynamic Neural Networks (Oral), 2022 | 5 | 2022 |
Instructive Decoding: Instruction-Tuned Large Language Models are Self-Refiner from Noisy Instructions T Kim, J Kim, G Lee, SY Yun ICLR 2024 (Spotlight), 2023 | 3* | 2023 |
Efficient Model for Image Classification With Regularization Tricks T Kim, J Kim, S Yun NeurIPS 2019 Competition and Demonstration Track (PMLR 123), 2020 | 3 | 2020 |
Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition T Kim, S Kang, H Shin, D Yoon, S Eom, K Shin, SY Yun NeurIPS 2022 Workshop: Weather4Cast Competition, 2022 | 2 | 2022 |
Revisiting Architecture-aware Knowledge Distillation: Smaller Models and Faster Search T Kim, H Myeong, SY Yun ICML 2022 Workshop: Hardware Aware Efficient Training (HAET), 2022 | 2 | 2022 |
Adaptive Local Bayesian Optimization Over Multiple Discrete Variables T Kim, J Ahn, N Kim, S Yun NeurIPS 2020 Workshop at Competition Track: Black-Box Optimization Challenge, 2020 | 2 | 2020 |
Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Federated Object Detection T Kim, E Lin, J Lee, C Lau, V Mugunthan NeurIPS 2023, 2023 | 1* | 2023 |
Revisiting the Activation Function for Federated Image Classification J Shin, T Kim, SY Yun NeurIPS 2022 Workshop: Federated Learning, 2022 | 1 | 2022 |
Layover Intermediate Layer for Multi-Label Classification in Efficient Transfer Learning S Eom, T Kim, SY Yun NeurIPS 2022 Workshop: Has it Trained Yet?, 2022 | 1 | 2022 |
Towards Fast Inference: Exploring and Improving Blockwise Parallel Drafts T Kim, AT Suresh, K Papineni, M Riley, S Kumar, A Benton arXiv, 2024 | | 2024 |
Non-linear Fusion in Federated Learning: A Hypernetwork Approach to Federated Domain Generalization M Bartholet, T Kim, A Beuret, SY Yun, JM Buhmann arXiv, 2024 | | 2024 |
Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labels T Kim, D Kim, SY Yun arXiv, 2024 | | 2024 |
Leveraging Normalization Layer in Adapters With Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot Learning Y Yang, T Kim, SY Yun AAAI 2024, 2023 | | 2023 |