Siegfried Nijssen

Artificial Intelligence · 2004–2020

A researcher in the Science corpus: 12 papers; trusts 52 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #4,615 of 35,345 by trust here, #12,244 by citations
• Information Systems — 35 citations into it; #262 of 8,771 by trust here, #948 by citations
• Computational Theory and Mathematics — 20 citations into it; #894 of 12,182 by trust here, #1,890 by citations
• Signal Processing — 20 citations into it; #1,567 of 7,572 by trust here, #4,560 by citations
• Algorithms & Complexity — 19 citations into it; #98 of 516 by trust here, #155 by citations
• Statistical Learning Theory — 19 citations into it
• Computer Networks and Communications — 13 citations into it; #1,353 of 9,886 by trust here, #2,852 by citations
• Sociology and Political Science — 5 citations into it

Whom Siegfried Nijssen trusts overall

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

1. J. Ross Quinlan
2. Mohammed J. Zaki
3. Jiawei Han
4. Rakesh Agrawal
5. Niko Speybroeck
6. Peter D. Turney
7. Pierangela Samarati
8. Arthur U. Asuncion
9. Xifeng Yan
10. Jian Pei

Who trusts Siegfried Nijssen overall

1. Emir Demirovi'c
2. Cynthia Rudin
3. Gaël Aglin
4. Mukund Deshpande
5. Mohammed J. Zaki
6. Anna Lukina
7. Peter James Stuckey
8. Vinícius G. Costa
9. Sina Aghaei
10. Arwa Bin Raies

Papers from Siegfried Nijssen's perspective

Ordered by how strongly the researchers Siegfried Nijssen 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. Attention is All you Need (Neural Information Processing Systems)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet classification with deep convolutional neural networks
5. Gradient-based learning applied to document recognition
6. Induction of decision trees
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Optimal constraint-based decision tree induction from itemset lattices (2010) — 72 citations
• Mining optimal decision trees from itemset lattices (2007) — 92 citations
• Learning optimal decision trees using constraint programming (2020) — 97 citations
• Constraint programming for itemset mining (2008) — 171 citations
• Constraint Programming for Data Mining and Machine Learning (2010) — 71 citations
• PyDL8.5: a Library for Learning Optimal Decision Trees (2020) — 33 citations
• Learning Optimal Decision Trees Using Caching Branch-and-Bound Search (2020) — 139 citations
• Encyclopedia of Machine Learning and Data Mining (2017) — 452 citations
• Correlated itemset mining in ROC space: a constraint programming approach (2009) — 83 citations
• Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations (2005) — 42 citations
• Frequent Subtree Mining – An Overview (2004) — 320 citations
• A quickstart in frequent structure mining can make a difference (2004) — 561 citations