Matthias Dehmer

Artificial Intelligence · 2008–2020

A researcher in the Science corpus: 7 papers; trusts 41 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it
• Molecular Biology — 29 citations into it; #3,700 of 17,773 by trust here, #10,506 by citations
• Model Architectures — 23 citations into it
• Computational Theory and Mathematics — 17 citations into it; #7,421 of 12,182 by trust here, #4,199 by citations
• Natural Language Processing — 13 citations into it
• Statistical and Nonlinear Physics — 11 citations into it; #2,568 of 10,127 by trust here, #7,024 by citations
• Signal Processing — 9 citations into it

Whom Matthias Dehmer trusts overall

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

1. U. Brandes
2. Peter Rowlinson
3. Stanley Wasserman
4. Roberto Todeschini
5. Huaiyu Zhu
6. Robert E. Ulanowicz
7. Geoffrey E. Hinton
8. Katherine Faust
9. Viviana Consonni
10. Yoshua Bengio

Who trusts Matthias Dehmer overall

1. Fabián Riquelme
2. Sergey V. Krivovichev
3. Lavanya Sivakumar
4. Pablo Gonzalez Cantergiani
5. Martin Grabner
6. Valerio De Biagi
7. C. Brühl
8. Muhammad Kamran Siddiqui

Papers from Matthias Dehmer's perspective

Ordered by how strongly the researchers Matthias Dehmer 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Going deeper with convolutions
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. GENERATIVE ADVERSARIAL NETS

Their papers

• A review of connectivity map and computational approaches in pharmacogenomics (2017) — 251 citations
• Information processing in complex networks: Graph entropy and information functionals (2008) — 302 citations
• A history of graph entropy measures (2011) — 556 citations
• An Introductory Review of Deep Learning for Prediction Models With Big Data (2020) — 492 citations
• The Discrimination Power of Molecular Identification Numbers Revisited (2013) — 78 citations
• Uniquely Discriminating Molecular Structures Using Novel Eigenvalue-Based Descriptors (2012) — 73 citations
• Entropy Bounds for Hierarchical Molecular Networks (2008) — 363 citations