| Xlnet: Generalized autoregressive pretraining for language understanding Z Yang, Z Dai, Y Yang, J Carbonell, RR Salakhutdinov, QV Le Advances in neural information processing systems 32, 2019 | 8422 | 2019 |
| Transformer-xl: Attentive language models beyond a fixed-length context Z Dai, Z Yang, Y Yang, J Carbonell, QV Le, R Salakhutdinov arXiv preprint arXiv:1901.02860, 2019 | 3485 | 2019 |
| Revisiting semi-supervised learning with graph embeddings Z Yang, W Cohen, R Salakhudinov International conference on machine learning, 40-48, 2016 | 1829 | 2016 |
| HotpotQA: A dataset for diverse, explainable multi-hop question answering Z Yang, P Qi, S Zhang, Y Bengio, WW Cohen, R Salakhutdinov, ... arXiv preprint arXiv:1809.09600, 2018 | 1407 | 2018 |
| Multi-task cross-lingual sequence tagging from scratch Z Yang, R Salakhutdinov, W Cohen arXiv preprint arXiv:1603.06270, 2016 | 609* | 2016 |
| Differentiable learning of logical rules for knowledge base reasoning F Yang, Z Yang, WW Cohen Advances in neural information processing systems 30, 2017 | 568 | 2017 |
| Good semi-supervised learning that requires a bad gan Z Dai, Z Yang, F Yang, WW Cohen, RR Salakhutdinov Advances in neural information processing systems 30, 2017 | 527 | 2017 |
| GPT understands, too X Liu, Y Zheng, Z Du, M Ding, Y Qian, Z Yang, J Tang AI Open, 2023 | 514* | 2023 |
| P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks X Liu, K Ji, Y Fu, WL Tam, Z Du, Z Yang, J Tang arXiv preprint arXiv:2110.07602, 2021 | 489* | 2021 |
| Gated-Attention Readers for Text Comprehension B Dhingra, H Liu, Z Yang, WW Cohen, R Salakhutdinov arXiv preprint arXiv:1606.01549, 2016 | 441 | 2016 |
| Review networks for caption generation Z Yang, Y Yuan, Y Wu, WW Cohen, RR Salakhutdinov Advances in neural information processing systems 29, 2016 | 385* | 2016 |
| Breaking the softmax bottleneck: A high-rank RNN language model Z Yang, Z Dai, R Salakhutdinov, WW Cohen arXiv preprint arXiv:1711.03953, 2017 | 368 | 2017 |
| Glm: General language model pretraining with autoregressive blank infilling Z Du, Y Qian, X Liu, M Ding, J Qiu, Z Yang, J Tang arXiv preprint arXiv:2103.10360, 2021 | 343* | 2021 |
| Cosnet: Connecting heterogeneous social networks with local and global consistency Y Zhang, J Tang, Z Yang, J Pei, PS Yu Proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015 | 333 | 2015 |
| Neural cross-lingual named entity recognition with minimal resources J Xie, Z Yang, G Neubig, NA Smith, J Carbonell arXiv preprint arXiv:1808.09861, 2018 | 187 | 2018 |
| Semi-supervised QA with generative domain-adaptive nets Z Yang, J Hu, R Salakhutdinov, WW Cohen arXiv preprint arXiv:1702.02206, 2017 | 176 | 2017 |
| Linguistic knowledge as memory for recurrent neural networks B Dhingra, Z Yang, WW Cohen, R Salakhutdinov arXiv preprint arXiv:1703.02620, 2017 | 146* | 2017 |
| Words or characters? fine-grained gating for reading comprehension Z Yang, B Dhingra, Y Yuan, J Hu, WW Cohen, R Salakhutdinov arXiv preprint arXiv:1611.01724, 2016 | 95 | 2016 |
| 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 | 66* | 2018 |
| Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x Q Zheng, X Xia, X Zou, Y Dong, S Wang, Y Xue, Z Wang, L Shen, A Wang, ... arXiv preprint arXiv:2303.17568, 2023 | 52 | 2023 |
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
