| Generating natural language adversarial examples through probability weighted word saliency S Ren, Y Deng, K He*, W Che 57th Annual Meeting of the Association for Computational Linguistics, 1085-1097, 2019 | 803 | 2019 |
| Nesterov accelerated gradient and scale invariance for adversarial attacks J Lin, C Song, K He*, L Wang, JE Hopcroft International Conference on Learning Representations, 2020 | 701 | 2020 |
| Enhancing the transferability of adversarial attacks through variance tuning X Wang, K He Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 476 | 2021 |
| Admix: Enhancing the transferability of adversarial attacks X Wang, X He, J Wang, K He International Conference on Computer Vision, ICCV 2021, 2021 | 266 | 2021 |
| Adversarially robust generalization just requires more unlabeled data R Zhai, T Cai, D He, C Dan, K He, J Hopcroft, L Wang arXiv preprint arXiv:1906.00555, 2019 | 176 | 2019 |
| TimeTraveler: Reinforcement learning for temporal knowledge graph forecasting H Sun, J Zhong, Y Ma, Z Han, K He The 2021 Conference on Empirical Methods in Natural Language Processing, 2021 | 175 | 2021 |
| Natural language adversarial defense through synonym encoding X Wang, H Jin, Y Yang, K He 37th Conference on Uncertainty in Artificial Intelligence, 2021 | 174* | 2021 |
| Single image reflection removal through cascaded refinement C Li, Y Yang, K He*, S Lin, JE Hopcroft Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 172 | 2020 |
| Uncovering the small community structure in large networks: A local spectral approach Y Li, K He, D Bindel, JE Hopcroft Proceedings of the 24th International Conference on World Wide Web, 658-668, 2015 | 169 | 2015 |
| NAGphormer: A tokenized graph transformer for node classification in large graphs J Chen, K Gao, G Li, K He The Eleventh International Conference on Learning Representations, 2023 | 165 | 2023 |
| Improving the generalization of adversarial training with domain adaptation C Song, K He*, L Wang, JE Hopcroft International Conference on Learning Representations, 2019 | 157 | 2019 |
| Multi-objective particle swarm optimization algorithm based on objective space division for the unequal-area facility layout problem J Liu, H Zhang, K He, S Jiang Expert Systems with Applications 102, 179-192, 2018 | 131 | 2018 |
| Towards understanding learning representations: To what extent do different neural networks learn the same representation L Wang, L Hu, J Gu, Z Hu, Y Wu, K He, J Hopcroft Advances in Neural Information Processing Systems 31, 2018 | 129 | 2018 |
| Stochastic variance reduced ensemble adversarial attack for boosting the adversarial transferability Y Xiong, J Lin, M Zhang, JE Hopcroft, K He Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 128 | 2022 |
| Hidden community detection in social networks K He, Y Li, S Soundarajan, JE Hopcroft Information Sciences 425, 92-106, 2018 | 124* | 2018 |
| A caving degree approach for the single container loading problem W Huang, K He European Journal of Operational Research 196 (1), 93-101, 2009 | 114 | 2009 |
| Local spectral clustering for overlapping community detection Y Li, K He, K Kloster, D Bindel, J Hopcroft ACM Transactions on Knowledge Discovery from Data 12 (2), 1-27, 2018 | 113 | 2018 |
| Robust local features for improving the generalization of adversarial training C Song, K He*, J Lin, L Wang, JE Hopcroft International Conference on Learning Representations, 2020 | 101 | 2020 |
| A powerful generative model using random weights for the deep image representation K He, Y Wang, J Hopcroft Advances in Neural Information Processing Systems 29, 631-639, 2016 | 98 | 2016 |
| Detecting overlapping communities from local spectral subspaces K He, Y Sun, D Bindel, J Hopcroft, Y Li 2015 IEEE International Conference on Data Mining, 769-774, 2015 | 92 | 2015 |
John HopcroftCornell UniversityВерификована је имејл адреса на cs.cornell.edu