| Fast and scalable polynomial kernels via explicit feature maps N Pham, R Pagh Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013 | 268 | 2013 |
| A near-linear time approximation algorithm for angle-based outlier detection in high-dimensional data N Pham, R Pagh Proceedings of the 18th ACM SIGKDD international conference on Knowledge …, 2012 | 126 | 2012 |
| Efficient estimation for high similarities using odd sketches M Mitzenmacher, R Pagh, N Pham Proceedings of the 23rd international conference on World wide web, 109-118, 2014 | 49 | 2014 |
| Online discovery of top-k similar motifs in time series data HT Lam, ND Pham, T Calders Proceedings of the 2011 SIAM International Conference on Data Mining, 1004-1015, 2011 | 37 | 2011 |
| Two novel adaptive symbolic representations for similarity search in time series databases ND Pham, QL Le, TK Dang 2010 12th International Asia-Pacific Web Conference, 181-187, 2010 | 29 | 2010 |
| I/O-efficient similarity join R Pagh, N Pham, F Silvestri, M Stöckel Algorithmica 78 (4), 1263-1283, 2017 | 23 | 2017 |
| HOT aSAX: A novel adaptive symbolic representation for time series discords discovery ND Pham, QL Le, TK Dang Asian Conference on Intelligent Information and Database Systems, 113-121, 2010 | 22 | 2010 |
| Scalability and total recall with fast CoveringLSH N Pham, R Pagh Proceedings of the 25th ACM International on Conference on Information and …, 2016 | 14 | 2016 |
| Hybrid LSH: faster near neighbors reporting in high-dimensional space N Pham arXiv preprint arXiv:1607.06179, 2016 | 9 | 2016 |
| L1-depth revisited: A robust angle-based outlier factor in high-dimensional space N Pham Joint European Conference on Machine Learning and Knowledge Discovery in …, 2018 | 4 | 2018 |
| On predicting student performance using low-rank matrix factorization techniques S Lorenzen, N Pham, S Alstrup European Conference on e-Learning, 326-334, 2017 | 4 | 2017 |
| DABAI: A data driven project for e-Learning in Denmark S Alstrup, C Hansen, C Hansen, N Hjuler, S Lorenzen, N Pham European Conference on e-Learning, 18-24, 2017 | 3 | 2017 |
| Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search SS Lorenzen, N Pham arXiv preprint arXiv:1908.08656, 2019 | 1 | 2019 |
| Sublinear Maximum Inner Product Search using Concomitants of Extreme Order Statistics N Pham arXiv preprint arXiv:2012.11098, 2020 | | 2020 |
Rasmus PaghProfessor of computer science, University of CopenhagenVerified email at di.ku.dk
