| Brecq: Pushing the limit of post-training quantization by block reconstruction Y Li, R Gong, X Tan, Y Yang, P Hu, Q Zhang, F Yu, W Wang, S Gu International Conference on Learning Representations 2021, 2021 | 337 | 2021 |
| Additive powers-of-two quantization: An efficient non-uniform discretization for neural networks Y Li, X Dong, W Wang International Conference on Learning Representations 2020, 2019 | 310 | 2019 |
| Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting S Deng, Y Li, S Zhang, S Gu International Conference on Learning Representations, 2022 | 214 | 2022 |
| Differentiable spike: Rethinking gradient-descent for training spiking neural networks Y Li, Y Guo, S Zhang, S Deng, Y Hai, S Gu Advances in Neural Information Processing Systems 34, 23426-23439, 2021 | 203 | 2021 |
| A free lunch from ANN: Towards efficient, accurate spiking neural networks calibration Y Li, S Deng, X Dong, R Gong, S Gu International Conference on Machine Learning, 6316-6325, 2021 | 177 | 2021 |
| Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization X Wei, R Gong, Y Li, X Liu, F Yu arXiv preprint arXiv:2203.05740, 2022 | 103 | 2022 |
| Diversifying sample generation for accurate data-free quantization X Zhang, H Qin, Y Ding, R Gong, Q Yan, R Tao, Y Li, F Yu, X Liu Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 94 | 2021 |
| Neural architecture search for spiking neural networks Y Kim, Y Li, H Park, Y Venkatesha, P Panda Proceedings of the 17th European Conference on Computer Vision (ECCV 2022), 2022 | 93 | 2022 |
| Neuromorphic Data Augmentation for Training Spiking Neural Networks Y Li, Y Kim, H Park, T Geller, P Panda Proceedings of the 17th European Conference on Computer Vision (ECCV 2022), 2022 | 79 | 2022 |
| Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling X Wei, Y Zhang, Y Li, X Zhang, R Gong, J Guo, X Liu EMNLP 2023, 2023 | 61 | 2023 |
| MQBench: Towards Reproducible and Deployable Model Quantization Benchmark Y Li, M Shen, J Ma, Y Ren, M Zhao, Q Zhang, R Gong, F Yu, J Yan Thirty-fifth Conference on Neural Information Processing Systems Datasets …, 2021 | 50 | 2021 |
| Exploring Lottery Ticket Hypothesis in Spiking Neural Networks Y Kim, Y Li, H Park, Y Venkatesha, R Yin, P Panda arXiv preprint arXiv:2207.01382, 2022 | 45 | 2022 |
| Once quantization-aware training: High performance extremely low-bit architecture search M Shen, F Liang, R Gong, Y Li, C Li, C Lin, F Yu, J Yan, W Ouyang Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 37* | 2021 |
| Mixmix: All you need for data-free compression are feature and data mixing Y Li, F Zhu, R Gong, M Shen, X Dong, F Yu, S Lu, S Gu Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 36* | 2021 |
| Rtn: Reparameterized ternary network Y Li, X Dong, SQ Zhang, H Bai, Y Chen, W Wang Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4780-4787, 2020 | 28 | 2020 |
| SEENN: Towards Temporal Spiking Early-Exit Neural Networks Y Li, T Geller, Y Kim, P Panda NeurIPS 2023, 2023 | 22 | 2023 |
| Error-Aware Conversion from ANN to SNN via Post-training Parameter Calibration Y Li, S Deng, X Dong, S Gu International Journal of Computer Vision, 1-24, 2024 | 21* | 2024 |
| Exploring Temporal Information Dynamics in Spiking Neural Networks Y Kim, Y Li, H Park, Y Venkatesha, A Hambitzer, P Panda AAAI 2023 (preprint arXiv:2211.14406), 2022 | 18 | 2022 |
| Efficient bitwidth search for practical mixed precision neural network Y Li, W Wang, H Bai, R Gong, X Dong, F Yu arXiv preprint arXiv:2003.07577, 2020 | 18 | 2020 |
| Workload-balanced pruning for sparse spiking neural networks R Yin, Y Kim, Y Li, A Moitra, N Satpute, A Hambitzer, P Panda IEEE Transactions on Emerging Topics in Computational Intelligence, 2024 | 10 | 2024 |
Priyadarshini (Priya) PandaAssistant Professor, Electrical Engineering, Yale University在 yale.edu 的电子邮件经过验证
