Arthur Zimek

Artificial Intelligence · 2008–2019

A researcher in the Science corpus: 14 papers; trusts 91 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 176 citations into it; #3,819 of 35,345 by trust here, #4,119 by citations
• Signal Processing — 95 citations into it; #1,742 of 7,572 by trust here, #1,411 by citations
• Computer Vision and Pattern Recognition — 48 citations into it
• Statistics and Probability — 39 citations into it; #1,912 of 12,932 by trust here, #1,506 by citations
• Computer Networks and Communications — 26 citations into it
• Biomedical Engineering — 25 citations into it; #2,644 of 10,734 by trust here, #7,279 by citations
• Information Systems — 25 citations into it; #5,120 of 8,771 by trust here, #2,667 by citations
• Molecular Biology — 21 citations into it

Whom Arthur Zimek trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Hans-Peter Kriegel
2. Peer Kröger
3. Fabrizio Angiulli
4. Wen Jin
5. Thomas G. Dietterich
6. Charu C. Aggarwal
7. Jiawei Han
8. Erich Schubert
9. Marina Meila
10. Dimitris Achlioptas

Who trusts Arthur Zimek overall

1. Ricardo José Gabrielli Barreto Campello
2. Hongzhi Wang
3. Charu C. Aggarwal
4. Hans-Peter Kriegel
5. Jörg Sander
6. Rémi Domingues
7. Davoud Moulavi
8. Erich Schubert
9. Tomás Pevný
10. Mohamed Jaward Bah

Papers from Arthur Zimek's perspective

Ordered by how strongly the researchers Arthur Zimek trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• LoOP: local outlier probabilities (2009) — 576 citations
• A survey on unsupervised outlier detection in high‐dimensional numerical data (2012) — 858 citations
• Angle-based outlier detection in high-dimensional data (2008) — 934 citations
• Interpreting and Unifying Outlier Scores (2011) — 300 citations
• Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data (2009) — 320 citations
• Ensembles for unsupervised outlier detection: challenges and research questions a position paper (2014) — 302 citations
• Density‐based clustering (2011) — 474 citations
• Density‐based clustering (2019) — 578 citations
• Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection (2015) — 853 citations
• Density-Based Clustering Validation (2014) — 330 citations
• Density-based Projected Clustering over High Dimensional Data Streams (2012) — 117 citations
• Subspace clustering (2012) — 896 citations
• Can Shared-Neighbor Distances Defeat the Curse of Dimensionality? (2010) — 305 citations
• Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering (2009) — 1,255 citations