| Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon X Dong, S Chen, SJ Pan Advances in Neural Information Processing Systems 30 (NIPS 2017) pre-proceedings, 2017 | 585 | 2017 |
| Additive powers-of-two quantization: An efficient non-uniform discretization for neural networks Y Li*, X Dong*, W Wang International Conference on Learning Representations, 2019 | 351 | 2019 |
| 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 | 213 | 2021 |
| Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit? S Zhu, X Dong, H Su 2019 Conference on Computer Vision and Pattern Recognition, 2019 | 168 | 2019 |
| exBERT: Extending pre-trained models with domain-specific vocabulary under constrained training resources W Tai, HT Kung, X Dong, M Comiter, CF Kuo Findings of the Association for Computational Linguistics: EMNLP 2020, 1433-1439, 2020 | 121 | 2020 |
| Neural Mean Discrepancy for Efficient Out-of-Distribution Detection X Dong, J Guo, A Li, WT Ting, C Liu, HT Kung Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022 | 63 | 2022 |
| Full-stack optimization for accelerating cnns using powers-of-two weights with fpga validation B McDanel, SQ Zhang, HT Kung, X Dong Proceedings of the ACM International Conference on Supercomputing, 449-460, 2019 | 46 | 2019 |
| A Main/Subsidiary Network Framework for Simplifying Binary Neural Network Y Xu*, X Dong*, Y Li, H Su 2019 Conference on Computer Vision and Pattern Recognition, 2019 | 41 | 2019 |
| 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 | 37* | 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 | 33 | 2020 |
| Converting artificial neural networks to spiking neural networks via parameter calibration Y Li, S Deng, X Dong, S Gu arXiv preprint arXiv:2205.10121, 2022 | 29 | 2022 |
| TCMPR: TCM prescription recommendation based on subnetwork term mapping and deep learning X Dong, Y Zheng, Z Shu, K Chang, J Xia, Q Zhu, K Zhong, X Wang, ... BioMed research international 2022 (1), 4845726, 2022 | 29 | 2022 |
| SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems X Dong, B De Salvo, M Li, C Liu, Z Qu, HT Kung, Z Li Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022 | 29 | 2022 |
| Spherefed: Hyperspherical federated learning X Dong, SQ Zhang, A Li, HT Kung European Conference on Computer Vision, 165-184, 2022 | 27 | 2022 |
| Maestro: A memory-on-logic architecture for coordinated parallel use of many systolic arrays HT Kung, B McDanel, SQ Zhang, X Dong, CC Chen 2019 IEEE 30th International Conference on Application-specific Systems …, 2019 | 27 | 2019 |
| Is heterogeneity notorious? taming heterogeneity to handle test-time shift in federated learning Y Tan, C Chen, W Zhuang, X Dong, L Lyu, G Long Advances in Neural Information Processing Systems 36, 27167-27180, 2023 | 22 | 2023 |
| Privacy Vulnerability of Split Computing to Data-Free Model Inversion Attacks X Dong, HX Yin, JM Alvarez, J Kautz, P Molchanov, HT Kung British Machine Vision Conference, 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 |
| Training for multi-resolution inference using reusable quantization terms SQ Zhang, B McDanel, HT Kung, X Dong Proceedings of the 26th ACM International Conference on Architectural …, 2021 | 14 | 2021 |
| A deeper look at depth pruning of llms SA Siddiqui, X Dong, G Heinrich, T Breuel, J Kautz, D Krueger, ... arXiv preprint arXiv:2407.16286, 2024 | 6 | 2024 |
Yuhang LiYale UniversityE-mail megerősítve itt: yale.edu