| Marginalized denoising auto-encoders for nonlinear representations M Chen, K Weinberger, F Sha, Y Bengio International conference on machine learning, 1476-1484, 2014 | 1120 | 2014 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 617 | 2024 |
| Top-k off-policy correction for a REINFORCE recommender system M Chen, A Beutel, P Covington, S Jain, F Belletti, EH Chi Proceedings of the Twelfth ACM International Conference on Web Search and …, 2019 | 526 | 2019 |
| Co-training for domain adaptation M Chen, KQ Weinberger, J Blitzer Advances in neural information processing systems 24, 2011 | 522 | 2011 |
| Fast image tagging M Chen, A Zheng, K Weinberger International conference on machine learning, 1274-1282, 2013 | 288 | 2013 |
| AntisymmetricRNN: A dynamical system view on recurrent neural networks B Chang, M Chen, E Haber, EH Chi arXiv preprint arXiv:1902.09689, 2019 | 258 | 2019 |
| Learning with marginalized corrupted features L Maaten, M Chen, S Tyree, K Weinberger International Conference on Machine Learning, 410-418, 2013 | 205 | 2013 |
| Efficient vector representation for documents through corruption M Chen arXiv preprint arXiv:1707.02377, 2017 | 170 | 2017 |
| Cost-sensitive tree of classifiers Z Xu, M Kusner, K Weinberger, M Chen International conference on machine learning, 133-141, 2013 | 151 | 2013 |
| Dynamical isometry and a mean field theory of RNNs: Gating enables signal propagation in recurrent neural networks M Chen, J Pennington, S Schoenholz International Conference on Machine Learning, 873-882, 2018 | 132 | 2018 |
| Automatic Feature Decomposition for Single View Co-training. M Chen, KQ Weinberger, Y Chen ICML 2 (3), 5, 2011 | 127 | 2011 |
| Classifier cascades and trees for minimizing feature evaluation cost Z Xu, MJ Kusner, KQ Weinberger, M Chen, O Chapelle The Journal of Machine Learning Research 15 (1), 2113-2144, 2014 | 110 | 2014 |
| Off-policy learning in two-stage recommender systems J Ma, Z Zhao, X Yi, J Yang, M Chen, J Tang, L Hong, EH Chi Proceedings of The Web Conference 2020, 463-473, 2020 | 108 | 2020 |
| Classifier cascade for minimizing feature evaluation cost M Chen, Z Xu, K Weinberger, O Chapelle, D Kedem Artificial Intelligence and Statistics, 218-226, 2012 | 102* | 2012 |
| F. Sha. Marginalized denoising autoencoders for domain adaptation M Chen, Z Xu, K Weinberger Proceedings of the 29th International Conference on Machine Learning, 767-774, 2012 | 91 | 2012 |
| Towards neural mixture recommender for long range dependent user sequences J Tang, F Belletti, S Jain, M Chen, A Beutel, C Xu, E H. Chi The World Wide Web Conference, 1782-1793, 2019 | 87 | 2019 |
| Medical data mining for early deterioration warning in general hospital wards Y Mao, Y Chen, G Hackmann, M Chen, C Lu, M Kollef, TC Bailey 2011 IEEE 11th International Conference on Data Mining Workshops, 1042-1049, 2011 | 67 | 2011 |
| User response models to improve a reinforce recommender system M Chen, B Chang, C Xu, EH Chi Proceedings of the 14th ACM international conference on web search and data …, 2021 | 59 | 2021 |
| Surrogate for long-term user experience in recommender systems Y Wang, M Sharma, C Xu, S Badam, Q Sun, L Richardson, L Chung, ... Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and …, 2022 | 54 | 2022 |
| Marginalizing stacked linear denoising autoencoders M Chen, KQ Weinberger, Z Xu, F Sha The Journal of Machine Learning Research 16 (1), 3849-3875, 2015 | 52 | 2015 |
Zhixiang (Eddie) XuWashington UniversityDirección de correo verificada de cse.wustl.edu
