| Scaling learning algorithms toward AI Y Bengio, Y LeCun | 1918 | 2007 |
| Training invariant support vector machines D DeCoste, B Schölkopf Machine learning 46, 161-190, 2002 | 860 | 2002 |
| HD-CNN: hierarchical deep convolutional neural networks for large scale visual recognition Z Yan, H Zhang, R Piramuthu, V Jagadeesh, D DeCoste, W Di, Y Yu Proceedings of the IEEE international conference on computer vision, 2740-2748, 2015 | 616* | 2015 |
| Machine learning for science: state of the art and future prospects E Mjolsness, D DeCoste science 293 (5537), 2051-2055, 2001 | 483 | 2001 |
| A modified finite Newton method for fast solution of large scale linear SVMs. SS Keerthi, D DeCoste, T Joachims Journal of Machine Learning Research 6 (3), 2005 | 417 | 2005 |
| Building support vector machines with reduced classifier complexity. SS Keerthi, O Chapelle, D DeCoste, KP Bennett, E Parrado-Hernández Journal of Machine Learning Research 7 (7), 2006 | 382 | 2006 |
| Naïve filterbots for robust cold-start recommendations ST Park, D Pennock, O Madani, N Good, D DeCoste Proceedings of the 12th ACM SIGKDD international conference on Knowledge …, 2006 | 263 | 2006 |
| Large-Scale Kernel Machines Y Bottou MIT Press, 2007 | 227 | 2007 |
| Dynamic across-time measurement interpretation D DeCoste Artificial Intelligence 51 (1-3), 273-341, 1991 | 167 | 1991 |
| Collaborative prediction using ensembles of maximum margin matrix factorizations D DeCoste Proceedings of the 23rd international conference on Machine learning, 249-256, 2006 | 137 | 2006 |
| Alpha seeding for support vector machines D DeCoste, K Wagstaff Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000 | 110 | 2000 |
| An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels MMS Lee, SS Keerthi, CJ Ong, D DeCoste IEEE Transactions on Neural Networks 15 (3), 750-757, 2004 | 97 | 2004 |
| Visualizing data mining models K Thearling, B Becker, D DeCoste, WD Mawby, M Pilote, D Sommerfield Information visualization in data mining and knowledge discovery, 205-222, 2001 | 93 | 2001 |
| Data parameters: A new family of parameters for learning a differentiable curriculum S Saxena, O Tuzel, D DeCoste Advances in Neural Information Processing Systems 32, 2019 | 92 | 2019 |
| Compact random feature maps R Hamid, Y Xiao, A Gittens, D DeCoste International conference on machine learning, 19-27, 2014 | 91 | 2014 |
| HD-CNN: Hierarchical deep convolutional neural network for image classification Z Yan, V Jagadeesh, D DeCoste, W Di, R Piramuthu International Conference on Computer Vision (ICCV) 2, 435-443, 2015 | 60 | 2015 |
| Large-scale kernel machines Y Bengio, Y LeCun Scaling Learning Algorithms towards AI. MIT Press, Cambridge, 2007 | 60 | 2007 |
| Ensembles of nearest neighbor forecasts D Yankov, D DeCoste, E Keogh European conference on machine learning, 545-556, 2006 | 60 | 2006 |
| Percentage based online advertising GW Flake, BD Brewer, DM DeCoste US Patent App. 11/734,134, 2008 | 58 | 2008 |
| Stochastic weight averaging in parallel: Large-batch training that generalizes well V Gupta, SA Serrano, D DeCoste arXiv preprint arXiv:2001.02312, 2020 | 50 | 2020 |
Olivier ChapelleGoogleEmail confirmado em chapelle.cc
