| Fast and scalable polynomial kernels via explicit feature maps N Pham, R Pagh Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013 | 403 | 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 | 155 | 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 | 71 | 2014 |
| 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 | 50 | 2010 |
| 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 | 45 | 2011 |
| HOT aSAX: A Novel Adaptive Symbolic Representation for Time Series Discords Discovery ND Pham, QL Le, TK Dang Intelligent Information and Database Systems: Second International …, 2010 | 30 | 2010 |
| Scalability and total recall with fast CoveringLSH N Pham, R Pagh Proceedings of the 25th ACM International on Conference on Information and …, 2016 | 17 | 2016 |
| I/O-efficient similarity join R Pagh, N Pham, F Silvestri, M Stöckel Algorithms-ESA 2015: 23rd Annual European Symposium, Patras, Greece …, 2015 | 17 | 2015 |
| I/O-efficient similarity join R Pagh, N Pham, F Silvestri, M Stöckel Algorithmica 78 (4), 1263-1283, 2017 | 16 | 2017 |
| Simple yet efficient algorithms for maximum inner product search via extreme order statistics N Pham Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 14 | 2021 |
| Revisiting wedge sampling for budgeted maximum inner product search SS Lorenzen, N Pham Joint European Conference on Machine Learning and Knowledge Discovery in …, 2020 | 12 | 2020 |
| Hybrid LSH: faster near neighbors reporting in high-dimensional space N Pham arXiv preprint arXiv:1607.06179, 2016 | 10 | 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 | 9 | 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 | 9 | 2017 |
| Falconn++: A locality-sensitive filtering approach for approximate nearest neighbor search N Pham, T Liu Advances in Neural Information Processing Systems 35, 31186-31198, 2022 | 7 | 2022 |
| 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 | 6 | 2017 |
| Sublinear maximum inner product search using concomitants of extreme order statistics N Pham arXiv preprint arXiv:2012.11098, 2020 | 3 | 2020 |
| Scalable Density-based Clustering with Random Projections H Xu, N Pham arXiv preprint arXiv:2402.15679, 2024 | | 2024 |
| On Deploying Mobile Deep Learning to Segment COVID-19 PCR Test Tube Images T Xiang, R Dean, J Zhao, N Pham Pacific-Rim Symposium on Image and Video Technology, 394-407, 2023 | | 2023 |
| A Transductive Forest for Anomaly Detection with Few Labels J Zhang, N Pham, G Dobbie Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | | 2023 |
Rasmus PaghProfessor of computer science, University of CopenhagenDirección de correo verificada de di.ku.dk
