ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms M Aumüller, E Bernhardsson, A Faithfull International Conference on Similarity Search and Applications, 34-49, 2017 | 97 | 2017 |
Optimal partitioning for dual-pivot quicksort M Aumüller, M Dietzfelbinger ACM Transactions on Algorithms (TALG) 12 (2), 1-36, 2015 | 42 | 2015 |
Explicit and efficient hash families suffice for cuckoo hashing with a stash M Aumüller, M Dietzfelbinger, P Woelfel Algorithmica 70 (3), 428-456, 2014 | 33 | 2014 |
Parameter-free locality sensitive hashing for spherical range reporting TD Ahle, M Aumüller, R Pagh Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete …, 2017 | 30 | 2017 |
Distance-sensitive hashing M Aumüller, T Christiani, R Pagh, F Silvestri Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2018 | 17 | 2018 |
How good is multi-pivot quicksort? M Aumüller, M Dietzfelbinger, P Klaue ACM Transactions on Algorithms (TALG) 13 (1), 1-47, 2016 | 17 | 2016 |
Experimental variations of a theoretically good retrieval data structure M Aumüller, M Dietzfelbinger, M Rink European Symposium on Algorithms, 742-751, 2009 | 10 | 2009 |
Dual-pivot quicksort: optimality, analysis and zeros of associated lattice paths M Aumüller, M Dietzfelbinger, C Heuberger, D Krenn, H Prodinger arXiv preprint arXiv:1611.00258, 2016 | 9* | 2016 |
PUFFINN: parameterless and universally fast finding of nearest neighbors M Aumüller, T Christiani, R Pagh, M Vesterli arXiv preprint arXiv:1906.12211, 2019 | 7 | 2019 |
The role of local intrinsic dimensionality in benchmarking nearest neighbor search M Aumüller, M Ceccarello International Conference on Similarity Search and Applications, 113-127, 2019 | 6* | 2019 |
An alternative analysis of cuckoo hashing with a stash and realistic hash functions M Aumüller Technische Universität Ilmenau, 2010 | 4 | 2010 |
Fair Near Neighbor Search: Independent Range Sampling in High Dimensions M Aumüller, R Pagh, F Silvestri Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2020 | 3 | 2020 |
A Simple hash class with strong randomness properties in graphs and hypergraphs M Aumüller, M Dietzfelbinger, P Woelfel arXiv preprint arXiv:1611.00029, 2016 | 2 | 2016 |
On the Analysis of Two Fundamental Randomized Algorithms-Multi-Pivot Quicksort and Efficient Hash Functions M Aumüller | 2 | 2015 |
Simple and Fast BlockQuicksort using Lomuto's Partitioning Scheme M Aumüller, N Hass 2019 Proceedings of the Twenty-First Workshop on Algorithm Engineering and …, 2019 | 1 | 2019 |
On the Analysis of Two Fundamental Randomized Algorithms M Aumüller Doktorarbeit (Ph. D, 2015 | 1 | 2015 |
Sampling a Near Neighbor in High Dimensions--Who is the Fairest of Them All? M Aumüller, S Har-Peled, S Mahabadi, R Pagh, F Silvestri arXiv preprint arXiv:2101.10905, 2021 | | 2021 |
Reproducibility Companion Paper: Visual Sentiment Analysis for Review Images with Item-Oriented and User-Oriented CNN QT Truong, HW Lauw, M Aumüller, N Nitta Proceedings of the 28th ACM International Conference on Multimedia, 4444-4447, 2020 | | 2020 |
Similarity Search and Applications: 13th International Conference, SISAP 2020, Copenhagen, Denmark, September 30–October 2, 2020, Proceedings S Satoh, L Vadicamo, A Zimek, F Carrara, I Bartolini, M Aumüller, ... International Conference on Similarity Search and Applications, 2020 | | 2020 |
Running experiments with confidence and sanity M Aumüller, M Ceccarello International Conference on Similarity Search and Applications, 387-395, 2020 | | 2020 |