Christian Borgelt

Information Systems · 2002–2012

A researcher in the Science corpus: 6 papers; trusts 34 researchers and is trusted by 41 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 26 citations into it; #246 of 8,771 by trust here, #2,064 by citations
• Computational Theory and Mathematics — 20 citations into it; #407 of 12,182 by trust here, #2,251 by citations
• Signal Processing — 12 citations into it; #3,813 of 7,572 by trust here, #7,112 by citations
• Artificial Intelligence — 11 citations into it; #4,521 of 35,345 by trust here, #21,878 by citations
• Algorithms & Complexity — 8 citations into it; #101 of 516 by trust here, #298 by citations
• Computer Networks and Communications — 8 citations into it
• Information Retrieval & Search — 5 citations into it

Whom Christian Borgelt trusts overall

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

1. Rakesh Agrawal
2. Mohammed J. Zaki
3. Rayadurgam Srikant
4. Jiawei Han
5. Zijian Zheng
6. Tomasz Imielinski
7. Ron Kohavi
8. Srinivasan Parthasarathy
9. Jian Pei
10. Arun N. Swami

Who trusts Christian Borgelt overall

1. Till Quack
2. Tong Wang
3. Benjamin Letham
4. Cynthia Rudin
5. Sunny Dhamnani
6. Michihiro Kuramochi
7. Gösta Grahne
8. Bart Goethals
9. Tyler H. McCormick
10. Dhruv Singal

Papers from Christian Borgelt's perspective

Ordered by how strongly the researchers Christian Borgelt 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. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Mining association rules between sets of items in large databases
7. Gradient-based learning applied to document recognition
8. Fast Algorithms for Mining Association Rules
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Mining molecular fragments: finding relevant substructures of molecules (2002) — 511 citations
• An implementation of the FP-growth algorithm (2005) — 395 citations
• Induction of Association Rules: Apriori Implementation (2002) — 357 citations
• Frequent item set mining (2012) — 313 citations
• EFFICIENT IMPLEMENTATIONS OF APRIORI AND ECLAT (2003) — 513 citations
• Advances in Intelligent Data Analysis V (2003) — 51 citations