| XGBoost: A scalable tree boosting system T Chen, C Guestrin KDD'16, 785-794, 2016 | 8814 | 2016 |
| MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems T Chen, M Li, Y Li, M Lin, N Wang, M Wang, T Xiao, B Xu, C Zhang, ... LearningSys Workshop at Neural Information Processing Systems 2015, 2015 | 1612 | 2015 |
| Empirical evaluation of rectified activations in convolutional network B Xu, N Wang, T Chen, M Li arXiv preprint arXiv:1505.00853, 2015 | 1524 | 2015 |
| XGBoost: R-package T Chen, T He, M Benesty R package version 0.4-2, 1-4, 2015 | 563* | 2015 |
| Stochastic gradient hamiltonian monte carlo T Chen, E Fox, C Guestrin International conference on machine learning, 1683-1691, 2014 | 480 | 2014 |
| TVM: An Automated End-to-End Optimizing Compiler for Deep Learning T Chen, T Moreau, Z Jiang, H Shen, E Yan, L Wang, Y Hu, L Ceze, ... OSDI 2018, 2018 | 476* | 2018 |
| Net2Net: Accelerating learning via knowledge transfer T Chen, I Goodfellow, J Shlens ICLR 2016, 2015 | 376 | 2015 |
| Collaborative personalized tweet recommendation K Chen, T Chen, G Zheng, O Jin, E Yao, Y Yu Proceedings of the 35th international ACM SIGIR conference on Research and …, 2012 | 325 | 2012 |
| A complete recipe for stochastic gradient MCMC YA Ma, T Chen, E Fox Advances in neural information processing systems 28, 2917-2925, 2015 | 285 | 2015 |
| Training deep nets with sublinear memory cost T Chen, B Xu, C Zhang, C Guestrin arXiv preprint arXiv:1604.06174, 2016 | 260 | 2016 |
| SVDFeature: a toolkit for feature-based collaborative filtering T Chen, W Zhang, Q Lu, K Chen, Z Zheng, Y Yu The Journal of Machine Learning Research 13 (1), 3619-3622, 2012 | 209 | 2012 |
| Higgs boson discovery with boosted trees T Chen, T He Neural Information Processing Systems 2014 Workshop on High-energy Physics …, 2015 | 118 | 2015 |
| Learning to Optimize Tensor Programs T Chen, L Zheng, E Yan, Z Jiang, T Moreau, L Ceze, C Guestrin, ... Neural Information Processing Systems 2018, 2018 | 91 | 2018 |
| Optimizing top-n collaborative filtering via dynamic negative item sampling W Zhang, T Chen, J Wang, Y Yu Proceedings of the 36th international ACM SIGIR conference on Research and …, 2013 | 91 | 2013 |
| Feature-based matrix factorization T Chen, Z Zheng, Q Lu, W Zhang, Y Yu arXiv preprint arXiv:1109.2271, 2011 | 61 | 2011 |
| xgboost: extreme gradient boosting. R package version 0.71. 2 T Chen, T He, M Benesty, V Khotilovich, Y Tang, H Cho, K Chen, ... | 59 | 2018 |
| Local implicit feedback mining for music recommendation D Yang, T Chen, W Zhang, Q Lu, Y Yu Proceedings of the sixth ACM conference on Recommender systems, 91-98, 2012 | 57 | 2012 |
| Combining factorization model and additive forest for collaborative followee recommendation T Chen, L Tang, Q Liu, D Yang, S Xie, X Cao, C Wu, E Yao, Z Liu, Z Jiang, ... KDD CUP, 2012 | 57 | 2012 |
| General functional matrix factorization using gradient boosting T Chen, H Li, Q Yang, Y Yu International Conference on Machine Learning, 436-444, 2013 | 51 | 2013 |
| A hardware–software blueprint for flexible deep learning specialization T Moreau, T Chen, L Vega, J Roesch, E Yan, L Zheng, J Fromm, Z Jiang, ... IEEE Micro 39 (5), 8-16, 2019 | 47* | 2019 |
Carlos GuestrinAmazon Professor of Machine Learning, University of WashingtonEmail verificata su cs.washington.edu
