Christopher Morris
Artificial Intelligence · with Mutzel · 2017–2020
A researcher in the Science corpus: 7 papers; trusts 204 researchers and is trusted by 39 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 183 citations into it; #6,795 of 35,345 by trust here, #5,120 by citations
- Graph Learning — 172 citations into it; #187 of 693 by trust here, #121 by citations
- Computer Vision and Pattern Recognition — 99 citations into it; #13,347 of 21,575 by trust here, #13,542 by citations
- Computational Theory and Mathematics — 78 citations into it; #8,302 of 12,182 by trust here, #11,946 by citations
- Molecular Biology — 64 citations into it
- Materials Chemistry — 43 citations into it; #6,141 of 11,513 by trust here, #11,470 by citations
- Statistical and Nonlinear Physics — 35 citations into it; #2,699 of 10,127 by trust here, #626 by citations
- Computational Chemistry & Drug Discovery — 27 citations into it
Whom Christopher Morris trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Karsten M. Borgwardt
- Chih-Jen Lin
- Nino Shervashidze
- Nils M. Kriege
- Martin Grohe
- Diederik P. Kingma
- S. V. N. Vishwanathan
- Pinar Yanardag
- Geoffrey E. Hinton
- William L. Hamilton
Who trusts Christopher Morris overall
- Haggai Maron
- William L. Hamilton
- Cristian Bodnar
- Matthias Fey
- Vijay Prakash Dwivedi
- Zhengdao Chen
- Petra Mutzel
- Nils M. Kriege
- Fabrizio Frasca
- Gaurav Rattan
Papers from Christopher Morris's perspective
Ordered by how strongly the researchers Christopher Morris 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
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Hierarchical Graph Representation Learning with Differentiable Pooling (2018) — 2,540 citations
- TUDataset: A collection of benchmark datasets for learning with graphs (2020) — 1,113 citations
- A survey on graph kernels (2019) — 491 citations
- Towards a practical k-dimensional Weisfeiler-Leman algorithm (2019) — 13 citations
- Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings (2019) — 185 citations
- Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (2018) — 2,039 citations
- Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs (2017) — 92 citations