| Character-level convolutional networks for text classification X Zhang, J Zhao, Y LeCun Advances in neural information processing systems 28, 2015 | 7146 | 2015 |
| End to end learning for self-driving cars A (All equal contribution), M Bojarski, D Del Testa, D Dworakowski, ... NIPS 2016 Symposium, 2016 | 5422* | 2016 |
| BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer F Sun, J Liu, J Wu, C Pei, X Lin, W Ou, P Jiang Proceedings of the 28th ACM international conference on information and …, 2019 | 2111 | 2019 |
| Energy-based generative adversarial network J Zhao, M Mathieu, Y LeCun ICLR 2017, 2016 | 1616 | 2016 |
| Deep graph library: Towards efficient and scalable deep learning on graphs M Wang, L Yu, D Zheng, Q Gan, Y Gai, Z Ye, M Li, J Zhou, Q Huang, C Ma, ... arXiv preprint arXiv:1909.01315, 2019 | 797 | 2019 |
| Disentangling factors of variation in deep representation using adversarial training MF Mathieu, J Zhao, A Ramesh, P Sprechmann, Y LeCun Advances in Neural Information Processing Systems, 5041-5049, 2016 | 563 | 2016 |
| Levenshtein transformer J Gu, C Wang, J Zhao Advances in neural information processing systems 32, 2019 | 394 | 2019 |
| Stacked What-Where Auto-encoders YLC Junbo Zhao, Michael Mathieu, Ross Goroshin ICLR 2016, 2016 | 362* | 2016 |
| Adversarially Regularized Autoencoders J Zhao, Y Kim, K Zhang, AM Rush, Y LeCun ICML 2018, 2017 | 358 | 2017 |
| Pico: Contrastive label disambiguation for partial label learning H Wang, R Xiao, Y Li, L Feng, G Niu, G Chen, J Zhao International conference on learning representations, 2022 | 144 | 2022 |
| Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences J Huang, Y Xu, Y Xue, Y Huang, X Li, X Chen, Y Xu, D Zhang, P Zhang, ... Nature Biomedical Engineering 7 (6), 797-810, 2023 | 80 | 2023 |
| Adversarially regularized autoencoders for generating discrete structures Y Kim, K Zhang, AM Rush, Y LeCun arXiv preprint arXiv:1706.04223 4, 2.2-6.2, 2017 | 73 | 2017 |
| A critical look at the current train/test split in machine learning J Tan, J Yang, S Wu, G Chen, J Zhao arXiv preprint arXiv:2106.04525, 2021 | 65 | 2021 |
| Glomo: Unsupervised learning of transferable relational graphs Z Yang, J Zhao, B Dhingra, K He, WW Cohen, RR Salakhutdinov, ... Advances in Neural Information Processing Systems 31, 2018 | 64* | 2018 |
| Pianotree vae: Structured representation learning for polyphonic music Z Wang, Y Zhang, Y Zhang, J Jiang, R Yang, J Zhao, G Xia arXiv preprint arXiv:2008.07118, 2020 | 61 | 2020 |
| Prompt as triggers for backdoor attack: Examining the vulnerability in language models S Zhao, J Wen, LA Tuan, J Zhao, J Fu arXiv preprint arXiv:2305.01219, 2023 | 55 | 2023 |
| Promix: Combating label noise via maximizing clean sample utility R Xiao, Y Dong, H Wang, L Feng, R Wu, G Chen, J Zhao arXiv preprint arXiv:2207.10276, 2022 | 43 | 2022 |
| Maybe only 0.5% data is needed: A preliminary exploration of low training data instruction tuning H Chen, Y Zhang, Q Zhang, H Yang, X Hu, X Ma, Y Yanggong, J Zhao arXiv preprint arXiv:2305.09246, 2023 | 32 | 2023 |
| Deep graph library: towards efficient and scalable deep learning on graphs. CoRR abs/1909.01315 (2019) M Wang, L Yu, QG Da Zheng, Y Gai, Z Ye, M Li, J Zhou, Q Huang, C Ma, ... arXiv preprint arXiv:1909.01315, 2019 | 32 | 2019 |
| Solar: Sinkhorn label refinery for imbalanced partial-label learning H Wang, M Xia, Y Li, Y Mao, L Feng, G Chen, J Zhao Advances in neural information processing systems 35, 8104-8117, 2022 | 29 | 2022 |
Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York UniversityVerified email at cs.nyu.edu
