| Stacked Generative Adversarial Networks X Huang, Y Li, O Poursaeed, J Hopcroft, S Belongie IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 | 3575* | 2017 |
| Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks S Liang, Y Li, R Srikant International Conference on Learning Representation (ICLR'18), 2018 | 1745 | 2018 |
| Exploring the limits of weakly supervised pretraining D Mahajan, R Girshick, V Ramanathan, K He, M Paluri, Y Li, A Bharambe, ... Proceedings of the European conference on computer vision (ECCV), 181-196, 2018 | 1396 | 2018 |
| Snapshot Ensembles: Train 1, Get M for Free G Huang*, Y Li*, G Pleiss, Z Liu, JE Hopcroft, KQ Weinberger International Conference on Learning Representation (ICLR 2017), 2017 | 939 | 2017 |
| Energy-based Out-of-distribution Detection W Liu, X Wang, J Owens, Y Li Advances in Neural Information Processing Systems 33, 2020 | 756 | 2020 |
| Generalized out-of-distribution detection: A survey J Yang, K Zhou, Y Li, Z Liu arXiv preprint arXiv:2110.11334, 2021 | 438 | 2021 |
| Convergent Learning: Do different neural networks learn the same representations? Y Li, J Yosinski, J Clune, H Lipson, J Hopcroft International Conference on Learning Representation (ICLR), 2016 | 307 | 2016 |
| ReAct: Out-of-distribution Detection With Rectified Activations Y Sun, C Guo, Y Li Advances in Neural Information Processing Systems 34, 144-157, 2021 | 214 | 2021 |
| Out-of-Distribution Detection with Deep Nearest Neighbors Y Sun, Y Ming, X Zhu, Y Li In Proceedings of International Conference on Machine Learning (ICML), 2022 | 181 | 2022 |
| On the Importance of Gradients for Detecting Distributional Shifts in the Wild R Huang, A Geng, Y Li Advances in Neural Information Processing Systems (NeurIPS), 2021 | 170 | 2021 |
| VOS: Learning What You Don't Know by Virtual Outlier Synthesis X Du, Z Wang, M Cai, Y Li Proceedings of the International Conference on Learning Representations 1 (4), 8, 2022 | 150 | 2022 |
| 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 | 143 | 2015 |
| MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space R Huang, Y Li Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 126 | 2021 |
| A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges M Salehi, H Mirzaei, D Hendrycks, Y Li, MH Rohban, M Sabokrou Transactions on Machine Learning Research (TMLR), 2022 | 120 | 2022 |
| Mitigating Neural Network Overconfidence with Logit Normalization H Wei, R Xie, H Cheng, L Feng, B An, Y Li In Proceedings of International Conference on Machine Learning (ICML), 2022 | 102 | 2022 |
| Local spectral clustering for overlapping community detection Y Li, K He, K Kloster, D Bindel, J Hopcroft ACM Transactions on Knowledge Discovery from Data (TKDD) 12 (2), 1-27, 2018 | 101 | 2018 |
| Model Patching: Closing the Subgroup Performance Gap with Data Augmentation K Goel, A Gu, Y Li, C Ré International Conference on Learning Representation (ICLR), 2021 | 99 | 2021 |
| The Lifecycle and Cascade of Social Messaging Groups J Qiu, Y Li, J Tang, Z Lu, H Ye, B Chen, Q Yang, JE Hopcroft Proceedings of the 25th International Conference on World Wide Web, 311-320, 2016 | 99 | 2016 |
| ATOM: Robustifying Out-of-distribution Detection Using Outlier Mining J Chen, Y Li, X Wu, Y Liang, S Jha Proceedings of European Conference on Machine Learning and Principles and …, 2021 | 92 | 2021 |
| MOOD: Multi-level Out-of-distribution Detection Z Lin, SD Roy, Y Li Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 91 | 2021 |
John HopcroftCornell UniversityVerified email at cs.cornell.edu
