| Detecting Adversarial Samples from Artifacts R Feinman, RR Curtin, S Shintre, AB Gardner arXiv preprint arXiv:1703.00410, 2017 | 677 | 2017 |
| Armadillo: a template-based C++ library for linear algebra C Sanderson, RR Curtin Journal of Open Source Software 1 (26), 1--2, 2016 | 604 | 2016 |
| MLPACK: A Scalable C++ Machine Learning Library RR Curtin, JR Cline, NP Slagle, WB March, P Ram, NA Mehta, AG Gray Journal of Machine Learning Research 14, 801-805, 2013 | 205 | 2013 |
| A User-Friendly Hybrid Sparse Matrix Class in C++ C Sanderson, R Curtin The 2018 International Congress on Mathematical Software (ICMS 2018), 2018 | 86 | 2018 |
| mlpack 3: a fast, flexible machine learning library RR Curtin, M Edel, M Lozhnikov, Y Mentekidis, S Ghaisas, S Zhang Journal of Open Source Software 3 (26), 726, 2018 | 67 | 2018 |
| Detecting DGA domains with recurrent neural networks and side information RR Curtin, AB Gardner, S Grzonkowski, A Kleymenov, A Mosquera ARES 2019, 2019 | 51 | 2019 |
| Fast Exact Max-Kernel Search RR Curtin, P Ram, AG Gray Proceedings of the 2013 SIAM International Conference on Data Mining (SDM …, 2012 | 49 | 2012 |
| Tree-Independent Dual-Tree Algorithms RR Curtin, WB March, P Ram, DV Anderson, AG Gray, CL Isbell Jr Proceedings of The 30th International Conference on Machine Learning (ICML …, 2013 | 38 | 2013 |
| Armadillo: C++ linear algebra library C Sanderson, R Curtin, I Cullinan, D Bouzas, S Funiak http://arma.sourceforge.net, 2014 | 36* | 2014 |
| Dual-Tree Fast Exact Max-Kernel Search RR Curtin, P Ram Statistical Analysis and Data Mining 7, 229-253, 2014 | 29 | 2014 |
| Functional Aggregate Queries with Additive Inequalities MA Khamis, RR Curtin, B Moseley, HQ Ngo, X Nguyen, D Olteanu, ... ACM Transactions on Database Systems (TODS) 45 (4), 1-41, 2020 | 25 | 2020 |
| Practical Sparse Matrices in C++ with Hybrid Storage and Template-Based Expression Optimisation C Sanderson, R Curtin Mathematical and Computational Applications 24 (3), 70, 2019 | 17 | 2019 |
| A Dual-Tree Algorithm for Fast k-means Clustering With Large k RR Curtin SIAM International Conference on Data Mining (SDM '17), 2017 | 17 | 2017 |
| Faster dual-tree traversal for nearest neighbor search RR Curtin International Conference on Similarity Search and Applications (SISAP 2015 …, 2015 | 15 | 2015 |
| Plug-and-play dual-tree algorithm runtime analysis RR Curtin, D Lee, WB March, P Ram Journal of Machine Learning Research 16, 3269-3297, 2015 | 14 | 2015 |
| An Automatic Benchmarking System M Edel, A Soni, RR Curtin NIPS 2014 Workshop on Software Engineering for Machine Learning, 2014 | 13 | 2014 |
| Rk-means: Fast Clustering for Relational Data R Curtin, B Moseley, HQ Ngo, XL Nguyen, D Olteanu, M Schleich International Conference on Artificial Intelligence and Statistics (AISTATS), 2020 | 12 | 2020 |
| Fast approximate furthest neighbors with data-dependent candidate selection RR Curtin, AB Gardner International Conference on Similarity Search and Applications 2016 (SISAP …, 2016 | 12* | 2016 |
| ensmallen: a flexible C++ library for efficient function optimization S Bhardwaj, RR Curtin, M Edel, Y Mentekidis, C Sanderson arXiv preprint arXiv:1810.09361, 2018 | 11 | 2018 |
| An Open Source C++ Implementation of Multi-Threaded Gaussian Mixture Models, k-Means and Expectation Maximisation C Sanderson, R Curtin 11th International Conference on Signal Processing and Communication Systems …, 2017 | 11 | 2017 |
Parikshit RamIBM ResearchVerified email at ibm.com
