| Stacked Generative Adversarial Networks X Huang, Y Li, O Poursaeed, J Hopcroft, S Belongie IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 | 1726* | 2017 |
| 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 | 466 | 2018 |
| 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 | 430* | 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 | 363 | 2017 |
| 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'16), 2015 | 155 | 2015 |
| 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 | 93 | 2015 |
| The lifecycle and cascade of wechat 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 | 66 | 2016 |
| Deep manifold traversal: Changing labels with convolutional features JR Gardner, P Upchurch, MJ Kusner, Y Li, KQ Weinberger, K Bala, ... arXiv preprint arXiv:1511.06421, 2015 | 57 | 2015 |
| 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 | 57 | 2015 |
| 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 | 45* | 2018 |
| Understanding the loss surface of neural networks for binary classification S Liang, R Sun, Y Li, R Srikant arXiv preprint arXiv:1803.00909, 2018 | 41 | 2018 |
| In a world that counts: Clustering and detecting fake social engagement at scale Y Li, O Martinez, X Chen, Y Li, JE Hopcroft Proceedings of the 25th International Conference on World Wide Web, 111-120, 2016 | 32 | 2016 |
| Defense against adversarial images using web-scale nearest-neighbor search A Dubey, L Maaten, Z Yalniz, Y Li, D Mahajan Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019 | 26 | 2019 |
| Towards Measuring and Inferring User Interest From Gaze Y Li, P Xu, D Lagun, V Navalpakkam 26th International Conference on World Wide Web (WWW'17), 2017 | 24 | 2017 |
| Multicast capacity with max-min fairness for heterogeneous networks Y Li, Q Peng, X Wang IEEE/ACM Transactions on Networking 22 (2), 622-635, 2013 | 17 | 2013 |
| On multicast capacity and delay in cognitive radio mobile ad hoc networks J Zhang, Y Li, Z Liu, F Wu, F Yang, X Wang IEEE Transactions on Wireless Communications 14 (10), 5274-5286, 2015 | 13 | 2015 |
| Robust Out-of-distribution Detection in Neural Networks J Chen, Y Li, X Wu, Y Liang, S Jha arXiv preprint arXiv:2003.09711, 2020 | 8 | 2020 |
| MSURU: Large Scale E-commerce Image Classification with Weakly Supervised Search Data Y Tang, F Borisyuk, S Malreddy, Y Li, Y Liu, S Kirshner Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 3 | 2019 |
| Energy-based Out-of-distribution Detection W Liu, X Wang, J Owens, Y Li Advances in Neural Information Processing Systems 33, 2020 | 2 | 2020 |
| The lifecycle and cascade of social messaging groups J Qiu, Y Li, J Tang, Z Lu, H Ye, B Chen, Q Yang, J Hopcroft ACM annual conference, Montreal, Canada, 2016 | 2 | 2016 |
R SrikantUniversity of Illinois at Urbana-ChampaignVerified email at illinois.edu
