| 2022 roadmap on neuromorphic computing and engineering DV Christensen, R Dittmann, B Linares-Barranco, A Sebastian, ... Neuromorphic Computing and Engineering 2 (2), 022501, 2022 | 514 | 2022 |
| Hi-CMD: Hierarchical cross-modality disentanglement for visible-infrared person re-identification S Choi, S Lee, Y Kim, T Kim, C Kim Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 389 | 2020 |
| Domain adaptation without source data Y Kim, D Cho, K Han, P Panda, S Hong IEEE Transactions on Artificial Intelligence 2 (6), 508-518, 2021 | 226* | 2021 |
| Revisiting batch normalization for training low-latency deep spiking neural networks from scratch Y Kim, P Panda Frontiers in neuroscience, 1638, 2021 | 193 | 2021 |
| Neural architecture search for spiking neural networks Y Kim, Y Li, H Park, Y Venkatesha, P Panda European Conference on Computer Vision (ECCV) 2022, 2022 | 115 | 2022 |
| Optimizing Deeper Spiking Neural Networks for Dynamic Vision Sensing Y Kim, P Panda Neural Networks, 2021 | 114 | 2021 |
| Neuromorphic Data Augmentation for Training Spiking Neural Networks Y Li, Y Kim, H Park, T Geller, P Panda European Conference on Computer Vision (ECCV) 2022, 2022 | 109 | 2022 |
| Combinational class activation maps for weakly supervised object localization S Yang, Y Kim, Y Kim, C Kim Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020 | 95 | 2020 |
| 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 IEEE International Conference on Acoustics, Speech and Signal Processing …, 2022 | 93 | 2022 |
| Beyond classification: directly training spiking neural networks for semantic segmentation Y Kim, J Chough, P Panda Neuromorphic Computing and Engineering (arXiv preprint arXiv:2110.07742), 2021 | 77 | 2021 |
| Cnn-based semantic segmentation using level set loss Y Kim, S Kim, T Kim, C Kim Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2019 | 75 | 2019 |
| Federated Learning with Spiking Neural Networks Y Venkatesha, Y Kim, L Tassiulas, P Panda IEEE Transactions on Signal Processing, 2021 | 67 | 2021 |
| Visual explanations from spiking neural networks using inter-spike intervals Y Kim, P Panda Scientific reports 11 (1), 19037, 2021 | 63 | 2021 |
| 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 (ECCV) 2022 (Oral Presentation), 102-120, 2022 | 61 | 2022 |
| SATA: Sparsity-Aware Training Accelerator for Spiking Neural Networks R Yin, A Moitra, A Bhattacharjee, Y Kim, P Panda IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2022 | 55 | 2022 |
| SEENN: Towards Temporal Spiking Early-Exit Neural Networks Y Li, T Geller, Y Kim, P Panda NeurIPS 2023 (arXiv preprint arXiv:2304.01230), 2023 | 42 | 2023 |
| Privatesnn: privacy-preserving spiking neural networks Y Kim, Y Venkatesha, P Panda Proceedings of the AAAI Conference on Artificial Intelligence 36 (1), 1192-1200, 2022 | 37 | 2022 |
| Exploring Temporal Information Dynamics in Spiking Neural Networks Y Kim, Y Li, H Park, Y Venkatesha, A Hambitzer, P Panda AAAI2023 (arXiv preprint arXiv:2211.14406), 2023 | 34 | 2023 |
| Adaptive graph adversarial networks for partial domain adaptation Y Kim, S Hong IEEE Transactions on Circuits and Systems for Video Technology 32 (1), 172-182, 2021 | 31 | 2021 |
| NEAT: Nonlinearity aware training for accurate, energy-efficient, and robust implementation of neural networks on 1T-1R crossbars A Bhattacharjee, L Bhatnagar, Y Kim, P Panda IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2021 | 28 | 2021 |
Priyadarshini (Priya) PandaAssistant Professor, Electrical Engineering, Yale UniversityVahvistettu sähköpostiosoite verkkotunnuksessa yale.edu