Petra Mutzel
Artificial Intelligence · 2000–2020
A researcher in the Science corpus: 7 papers; trusts 131 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 109 citations into it; #8,754 of 35,345 by trust here, #13,962 by citations
- Graph Learning — 104 citations into it
- Computer Vision and Pattern Recognition — 52 citations into it; #6,675 of 21,575 by trust here, #7,938 by citations
- Computational Theory and Mathematics — 51 citations into it; #8,309 of 12,182 by trust here, #11,946 by citations
- Molecular Biology — 39 citations into it
- Statistical and Nonlinear Physics — 28 citations into it
- Materials Chemistry — 21 citations into it; #6,142 of 11,513 by trust here, #11,470 by citations
- Computational Chemistry & Drug Discovery — 17 citations into it
Whom Petra Mutzel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Karsten M. Borgwardt
- Chih-Jen Lin
- Nils M. Kriege
- Nino Shervashidze
- Martin Grohe
- S. V. N. Vishwanathan
- László Babai
- Christopher Morris
- Diederik P. Kingma
- Hans-Peter Kriegel
Who trusts Petra Mutzel overall
- Christopher Morris
- Steven S. Skiena
- Nils M. Kriege
- Haggai Maron
- Cristian Bodnar
- A. Manteuffel
- Fabrizio Frasca
- Giorgos Bouritsas
- Zhen Zhang
- Fredrik D. Johansson
Papers from Petra Mutzel's perspective
Ordered by how strongly the researchers Petra Mutzel trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- TUDataset: A collection of benchmark datasets for learning with graphs (2020) — 1,113 citations
- Towards a practical k-dimensional Weisfeiler-Leman algorithm (2019) — 13 citations
- A Linear Time Implementation of SPQR-Trees (2000) — 295 citations
- Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings (2019) — 185 citations
- Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs (2017) — 92 citations
- Subgraph Matching Kernels for Attributed Graphs (2012) — 319 citations
- Graph Drawing Software (2003) — 379 citations