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ORCIDNinh Pham
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- affiliation: University of Auckland, New Zealand
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2020 – today
- 2024
[i8]Haochuan Xu, Ninh Pham:
Scalable Density-based Clustering with Random Projections. CoRR abs/2402.15679 (2024)- 2023
- [c16]Jingrui Zhang, Ninh Pham
, Gillian Dobbie:
A Transductive Forest for Anomaly Detection with Few Labels. ECML/PKDD (1) 2023: 286-301 - [c15]Ting Xiang, Richard Dean, Jiawei Zhao, Ninh Pham:
On Deploying Mobile Deep Learning to Segment COVID-19 PCR Test Tube Images. PSIVT 2023: 394-407 - 2022
- [c14]Ninh Pham, Tao Liu:
Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search. NeurIPS 2022 - [i7]Kitty Li, Ninh Pham:
An Efficient Hashing-based Ensemble Method for Collaborative Outlier Detection. CoRR abs/2201.06806 (2022) - [i6]Ninh Pham, Tao Liu:
Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search. CoRR abs/2206.01382 (2022) - 2021
- [c13]Stephan Sloth Lorenzen, Ninh Pham:
Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search (Extended Abstract). IJCAI 2021: 4789-4793 - [c12]Ninh Pham:
Simple Yet Efficient Algorithms for Maximum Inner Product Search via Extreme Order Statistics. KDD 2021: 1339-1347 - 2020
- [c11]Stephan Sloth Lorenzen, Ninh Pham:
Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search. ECML/PKDD (1) 2020: 439-455 - [i5]Ninh Pham:
Sublinear Maximum Inner Product Search using Concomitants of Extreme Order Statistics. CoRR abs/2012.11098 (2020)
2010 – 2019
- 2019
- [i4]Stephan Sloth Lorenzen, Ninh Pham:
Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search. CoRR abs/1908.08656 (2019) - 2018
- [c10]Ninh Pham:
L1-Depth Revisited: A Robust Angle-Based Outlier Factor in High-Dimensional Space. ECML/PKDD (1) 2018: 105-121 - 2017
- [j1]Rasmus Pagh, Ninh Pham, Francesco Silvestri, Morten Stöckel:
I/O-Efficient Similarity Join. Algorithmica 78(4): 1263-1283 (2017) - [c9]Ninh Pham:
Hybrid LSH: Faster Near Neighbors Reporting in High-dimensional Space. EDBT 2017: 454-457 - 2016
- [c8]Ninh Pham, Rasmus Pagh:
Scalability and Total Recall with Fast CoveringLSH. CIKM 2016: 1109-1118 - [i3]Ninh Pham, Rasmus Pagh:
Scalability and Total Recall with Fast CoveringLSH. CoRR abs/1602.02620 (2016) - [i2]Ninh Pham:
Hybrid LSH: Faster Near Neighbors Reporting in High-dimensional Space. CoRR abs/1607.06179 (2016) - 2015
- [c7]Rasmus Pagh, Ninh Pham, Francesco Silvestri, Morten Stöckel:
I/O-Efficient Similarity Join. ESA 2015: 941-952 - [i1]Rasmus Pagh, Ninh Pham, Francesco Silvestri, Morten Stöckel:
I/O-Efficient Similarity Join. CoRR abs/1507.00552 (2015) - 2014
- [c6]Michael Mitzenmacher, Rasmus Pagh, Ninh Pham:
Efficient estimation for high similarities using odd sketches. WWW 2014: 109-118 - 2013
- [c5]Ninh Pham, Rasmus Pagh:
Fast and scalable polynomial kernels via explicit feature maps. KDD 2013: 239-247 - 2012
- [c4]Ninh Pham, Rasmus Pagh:
A near-linear time approximation algorithm for angle-based outlier detection in high-dimensional data. KDD 2012: 877-885 - 2011
- [c3]Hoang Thanh Lam, Toon Calders, Ninh Pham:
Online Discovery of Top-k Similar Motifs in Time Series Data. SDM 2011: 1004-1015 - 2010
- [c2]Ninh D. Pham, Quang Loc Le, Tran Khanh Dang:
HOT aSAX: A Novel Adaptive Symbolic Representation for Time Series Discords Discovery. ACIIDS (1) 2010: 113-121 - [c1]Ninh D. Pham, Quang Loc Le, Tran Khanh Dang:
Two Novel Adaptive Symbolic Representations for Similarity Search in Time Series Databases. APWeb 2010: 181-187
Coauthor Index
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