Nils M. Kriege
Artificial Intelligence · 2012–2020
A researcher in the Science corpus: 4 papers; trusts 112 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 69 citations into it; #8,269 of 35,345 by trust here, #9,760 by citations
- Graph Learning — 68 citations into it; #415 of 693 by trust here, #519 by citations
- Computer Vision and Pattern Recognition — 45 citations into it; #6,597 of 21,575 by trust here, #8,296 by citations
- Computational Theory and Mathematics — 33 citations into it
- Molecular Biology — 32 citations into it
- Materials Chemistry — 18 citations into it
- Computational Chemistry & Drug Discovery — 12 citations into it
- Statistical and Nonlinear Physics — 12 citations into it; #5,774 of 10,127 by trust here, #5,011 by citations
Whom Nils M. Kriege trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Karsten M. Borgwardt
- Kristen Grauman
- Chih-Jen Lin
- Nino Shervashidze
- David Haussler
- Trevor Darrell
- Thomas Gärtner
- S. V. N. Vishwanathan
- Risi Kondor
- Pierre Mahé
Who trusts Nils M. Kriege overall
- Christopher Morris
- Petra Mutzel
- Aynaz Taheri
- Cristian Bodnar
- Gaurav Rattan
- Fabrizio Frasca
- Giorgos Bouritsas
- Muhan Zhang
- Kristian Kersting
- Zhen Zhang
Papers from Nils M. Kriege's perspective
Ordered by how strongly the researchers Nils M. Kriege trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- TUDataset: A collection of benchmark datasets for learning with graphs (2020) — 1,113 citations
- A survey on graph kernels (2019) — 491 citations
- On Valid Optimal Assignment Kernels and Applications to Graph Classification (2016) — 224 citations
- Subgraph Matching Kernels for Attributed Graphs (2012) — 319 citations