| XGBoost: A scalable tree boosting system T Chen, C Guestrin KDD'16, 785-794, 2016 | 45701 | 2016 |
| Empirical evaluation of rectified activations in convolutional network B Xu arXiv preprint arXiv:1505.00853, 2015 | 4209 | 2015 |
| XGBoost: R-package T Chen, T He, M Benesty R package version 0.4-2, 1-4, 2015 | 4057* | 2015 |
| 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 | 2838 | 2015 |
| TVM: An Automated End-to-End Optimizing Compiler for Deep Learning T Chen, T Moreau, Z Jiang, L Zheng, E Yan, M Cowan, H Shen, L Wang, ... OSDI 2018, 2018 | 2156* | 2018 |
| Stochastic gradient hamiltonian monte carlo T Chen, E Fox, C Guestrin International conference on machine learning, 1683-1691, 2014 | 1089 | 2014 |
| Training deep nets with sublinear memory cost T Chen, B Xu, C Zhang, C Guestrin arXiv preprint arXiv:1604.06174, 2016 | 1010 | 2016 |
| Net2Net: Accelerating learning via knowledge transfer T Chen, I Goodfellow, J Shlens ICLR 2016, 2015 | 757 | 2015 |
| A complete recipe for stochastic gradient MCMC YA Ma, T Chen, E Fox Advances in neural information processing systems 28, 2015 | 584 | 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 | 458 | 2018 |
| 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 | 372 | 2012 |
| 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 | 280 | 2013 |
| 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 | 276 | 2012 |
| 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 | 267* | 2019 |
| Higgs boson discovery with boosted trees T Chen, T He Neural Information Processing Systems 2014 Workshop on High-energy Physics …, 2015 | 235 | 2015 |
| XGBoost: Extreme gradient boosting, 2021 T Chen, T He, M Benesty, V Khotilovich, Y Tang, H Cho, K Chen, ... R package version 1 (1.1), 2021 | 189 | 2021 |
| xgboost: extreme gradient boosting. R package version 0.71. 2 T Chen, T He, M Benesty, V Khotilovich, Y Tang, H Cho, K Chen, ... Grin Verlag: München, Germnay, 2018 | 184 | 2018 |
| Relay: A new ir for machine learning frameworks J Roesch, S Lyubomirsky, L Weber, J Pollock, M Kirisame, T Chen, ... Proceedings of the 2nd ACM SIGPLAN international workshop on machine …, 2018 | 130 | 2018 |
| Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv 2015 T Chen, M Li, Y Li, M Lin, N Wang, M Wang, T Xiao, B Xu, C Zhang, ... arXiv preprint arXiv:1512.01274, 0 | 87 | |
| Dynamic tensor rematerialization M Kirisame, S Lyubomirsky, A Haan, J Brennan, M He, J Roesch, T Chen, ... arXiv preprint arXiv:2006.09616, 2020 | 86 | 2020 |
Carlos GuestrinProfessor, Stanford Universitystanford.edu의 이메일 확인됨
