Markus Goldstein

Artificial Intelligence · with Dengel · 2012–2016

A researcher in the Science corpus: 4 papers; trusts 41 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #18,948 of 35,345 by trust here, #13,962 by citations
• Computer Networks and Communications — 18 citations into it; #6,607 of 9,886 by trust here, #3,318 by citations
• Statistics and Probability — 18 citations into it
• Signal Processing — 15 citations into it; #5,204 of 7,572 by trust here, #3,309 by citations
• Biomedical Engineering — 12 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Markus Goldstein trusts overall

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

1. Rfdo 2XWOLHUV
2. Hans-Peter Kriegel
3. Fabrizio Angiulli
4. Varun Chandola
5. Spiros E. Papadimitriou
6. Bernhard Schölkopf
7. Wen Jin
8. Kingsly Leung
9. Chih-Jen Lin
10. Naoki Abe

Who trusts Markus Goldstein overall

1. Hongzhi Wang
2. Sebastian Schmidl
3. Xiaoyi Gu
4. Mennatallah Amer
5. Zheng Li
6. Yue Zhao
7. Mohamed Jaward Bah
8. Alireza Shafaei
9. Edward Chou
10. Phillip Wenig

Papers from Markus Goldstein's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Automatic classifier selection for non-experts (2014) — 163 citations
• A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data (2016) — 983 citations
• Enhancing one-class support vector machines for unsupervised anomaly detection (2013) — 453 citations
• Histogram-based Outlier Score (HBOS): A fast Unsupervised Anomaly Detection Algorithm (2012) — 684 citations