| Meta batch-instance normalization for generalizable person re-identification S Choi, T Kim, M Jeong, H Park, C Kim Proceedings of the IEEE/CVF conference on Computer Vision and Pattern …, 2021 | 135 | 2021 |
| Neural architecture search for spiking neural networks Y Kim, Y Li, H Park, Y Venkatesha, P Panda European Conference on Computer Vision, 36-56, 2022 | 82 | 2022 |
| Neuromorphic data augmentation for training spiking neural networks Y Li, Y Kim, H Park, T Geller, P Panda European Conference on Computer Vision, 631-649, 2022 | 65 | 2022 |
| Robust federated learning with noisy labels S Yang, H Park, J Byun, C Kim IEEE Intelligent Systems 37 (2), 35-43, 2022 | 62 | 2022 |
| Rate coding or direct coding: Which one is better for accurate, robust, and energy-efficient spiking neural networks? Y Kim, H Park, A Moitra, A Bhattacharjee, Y Venkatesha, P Panda ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 48 | 2022 |
| Exploring lottery ticket hypothesis in spiking neural networks Y Kim, Y Li, H Park, Y Venkatesha, R Yin, P Panda European Conference on Computer Vision, 102-120, 2022 | 33 | 2022 |
| Exploring temporal information dynamics in spiking neural networks Y Kim, Y Li, H Park, Y Venkatesha, A Hambitzer, P Panda Proceedings of the AAAI Conference on Artificial Intelligence 37 (7), 8308-8316, 2023 | 12 | 2023 |
| Uncovering the representation of spiking neural networks trained with surrogate gradient Y Li, Y Kim, H Park, P Panda arXiv preprint arXiv:2304.13098, 2023 | 7 | 2023 |
| Wearable-based human activity recognition with spatio-temporal spiking neural networks Y Li, R Yin, H Park, Y Kim, P Panda arXiv preprint arXiv:2212.02233, 2022 | 6 | 2022 |
| Lottery ticket hypothesis for spiking neural networks Y Kim, Y Li, H Park, Y Venkatesha, R Yin, P Panda arXiv preprint arXiv:2207.01382, 2022 | 6 | 2022 |
| Binding touch to everything: Learning unified multimodal tactile representations F Yang, C Feng, Z Chen, H Park, D Wang, Y Dou, Z Zeng, X Chen, ... arXiv preprint arXiv:2401.18084, 2024 | 5 | 2024 |
| Addressing client drift in federated continual learning with adaptive optimization Y Venkatesha, Y Kim, H Park, Y Li, P Panda Available at SSRN 4188586, 2022 | 4 | 2022 |
| Self-training of graph neural networks using similarity reference for robust training with noisy labels H Park, M Jeong, Y Kim, C Kim 2020 IEEE International Conference on Image Processing (ICIP), 1951-1955, 2020 | 3 | 2020 |
| Test-Time Adaptation for Depth Completion H Park, A Gupta, A Wong arXiv preprint arXiv:2402.03312, 2024 | 1 | 2024 |
| Divide-and-conquer the NAS puzzle in resource-constrained federated learning systems Y Venkatesha, Y Kim, H Park, P Panda Neural Networks 168, 569-579, 2023 | 1 | 2023 |
| AugUndo: Scaling Up Augmentations for Unsupervised Depth Completion Y Wu, TY Liu, H Park, S Soatto, D Lao, A Wong arXiv preprint arXiv:2310.09739, 2023 | 1 | 2023 |
| WorDepth: Variational Language Prior for Monocular Depth Estimation Z Zeng, D Wang, F Yang, H Park, Y Wu, S Soatto, BW Hong, D Lao, ... arXiv preprint arXiv:2404.03635, 2024 | | 2024 |
| Do Spiking Neural Networks Learn Similar Representation with Artificial Neural Networks? A Pilot Study on SNN Representation Y Li, Y Kim, H Park, P Panda | | 2022 |
| A. Code Implementation Y Kim, Y Li, H Park, Y Venkatesha, P Panda | | |
| A. Backpropagation Training with Surrogate Gradients Y Kim, Y Li, H Park, Y Venkatesha, R Yin, P Panda | | |
Youngeun KimYale Universityyale.edu의 이메일 확인됨
