| Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures C Lee, SS Sarwar, P Panda, G Srinivasan, K Roy Frontiers in Neuroscience 14, 2020 | 431* | 2020 |
| X-sram: Enabling in-memory boolean computations in cmos static random access memories A Agrawal, A Jaiswal, C Lee, K Roy IEEE Transactions on Circuits and Systems I: Regular Papers, 1-14, 2018 | 272 | 2018 |
| Training deep spiking convolutional neural networks with stdp-based unsupervised pre-training followed by supervised fine-tuning C Lee, P Panda, G Srinivasan, K Roy Frontiers in neuroscience 12, 373945, 2018 | 206 | 2018 |
| Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks C Lee, A Kosta, AZ Zhu, K Chaney, K Daniilidis, K Roy In proceedings of European Conference on Computer Vision (ECCV), 2020, 2020 | 149 | 2020 |
| Deep spiking convolutional neural network trained with unsupervised spike-timing-dependent plasticity C Lee, G Srinivasan, P Panda, K Roy IEEE Transactions on Cognitive and Developmental Systems 11 (3), 384-394, 2018 | 141 | 2018 |
| A comprehensive analysis on adversarial robustness of spiking neural networks S Sharmin, P Panda, SS Sarwar, C Lee, W Ponghiran, K Roy 2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019 | 66 | 2019 |
| Towards understanding the effect of leak in spiking neural networks SS Chowdhury, C Lee, K Roy Neurocomputing 464, 83-94, 2021 | 42 | 2021 |
| Fusion-FlowNet: Energy-Efficient Optical Flow Estimation using Sensor Fusion and Deep Fused Spiking-Analog Network Architectures C Lee, AK Kosta, K Roy 2022 IEEE International Conference on Robotics and Automation (ICRA), 2022 | 31 | 2022 |
| Training Deep Spiking Neural Networks for Energy-Efficient Neuromorphic Computing G Srinivasan, C Lee, A Sengupta, P Panda, SS Sarwar, K Roy ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 25 | 2020 |
| X-CHANGR: Changing Memristive Crossbar Mapping for Mitigating Line-Resistance Induced Accuracy Degradation in Deep Neural Networks A Agrawal, C Lee, K Roy arXiv preprint arXiv:1907.00285, 2019 | 25 | 2019 |
| Exploring Spike-Based Learning for Neuromorphic Computing: Prospects and Perspectives N Rathi, A Agrawal, C Lee, AK Kosta, K Roy 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE), 902-907, 2021 | 10 | 2021 |
| Self-Supervised Optical Flow with Spiking Neural Networks and Event Based Cameras K Chaney, A Panagopoulou, C Lee, K Roy, K Daniilidis 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 9 | 2021 |
| Noise resilient leaky integrate-and-fire neurons based on multi-domain spintronic devices C Wang, C Lee, K Roy Scientific Reports 12 (1), 8361, 2022 | 6 | 2022 |
| ChatQA: Building GPT-4 Level Conversational QA Models Z Liu, W Ping, R Roy, P Xu, C Lee, M Shoeybi, B Catanzaro arXiv preprint arXiv:2401.10225, 2024 | 2 | 2024 |
Kaushik RoyProfessor of Electrical and Computer Engineering, Purdue UniversityVerified email at purdue.edu
