Oleksandr Shchur

Artificial Intelligence · 2018

A researcher in the Science corpus: 3 papers; trusts 43 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it; #9,374 of 35,345 by trust here, #8,619 by citations
• Graph Learning — 41 citations into it; #213 of 693 by trust here, #246 by citations
• Computer Vision and Pattern Recognition — 29 citations into it; #6,816 of 21,575 by trust here, #6,146 by citations
• Generative Models — 16 citations into it; #1,441 of 1,956 by trust here, #1,382 by citations
• Representation & Self-Supervised Learning — 16 citations into it
• Statistical and Nonlinear Physics — 16 citations into it; #2,663 of 10,127 by trust here, #2,074 by citations
• Molecular Biology — 12 citations into it
• Computational Mechanics — 11 citations into it

Whom Oleksandr Shchur trusts overall

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

1. Diederik P. Kingma
2. Aleksandar Bojchevski
3. Thomas Kipf
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Max Welling
7. Jimmy Ba
8. Eric R. Ziegel
9. Yann LeCun
10. Alex Krizhevsky

Who trusts Oleksandr Shchur overall

1. Johannes Klicpera
2. Lingxiao Zhao
3. Xiyuan Wang
4. Dongkwan Kim
5. Hongwei Wang
6. Erlin Pan
7. Benjamin Paul Chamberlain
8. Matthias Fey
9. Carl Yang
10. Benedek Rozemberczki

Papers from Oleksandr Shchur's perspective

Ordered by how strongly the researchers Oleksandr Shchur 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• NetGAN: Generating Graphs via Random Walks (2018) — 410 citations
• Pitfalls of Graph Neural Network Evaluation (2018) — 1,809 citations
• Dual-Primal Graph Convolutional Networks (2018) — 84 citations