Michael Sejr Schlichtkrull

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 7 papers; trusts 212 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 288 citations into it; #10,471 of 35,345 by trust here, #3,582 by citations
• Natural Language Processing — 167 citations into it; #5,194 of 5,688 by trust here, #3,478 by citations
• Evaluation & Benchmarks — 74 citations into it; #2,972 of 3,147 by trust here, #2,062 by citations
• Computer Vision and Pattern Recognition — 63 citations into it
• Safety, Robustness & Interpretability — 60 citations into it
• Sociology and Political Science — 58 citations into it
• Information Systems — 46 citations into it
• Graph Learning — 36 citations into it; #257 of 693 by trust here, #97 by citations

Whom Michael Sejr Schlichtkrull trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Jimmy Ba
7. Yann LeCun
8. Ronald J. Williams
9. Richard Socher
10. Max Welling

Who trusts Michael Sejr Schlichtkrull overall

1. Nicola De Cao
2. William L. Hamilton
3. Leonardo F. R. Ribeiro
4. Hao Yuan
5. Matthias Fey
6. Seyed Mehran Kazemi
7. Kun Zhou
8. Ziniu Hu
9. Woojeong Jin
10. Haitian Sun

Papers from Michael Sejr Schlichtkrull's perspective

Ordered by how strongly the researchers Michael Sejr Schlichtkrull 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Modeling Relational Data with Graph Convolutional Networks (2017) — 6,128 citations
• UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering (2020) — 106 citations
• A Survey on Automated Fact-Checking (2021) — 757 citations
• The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) Shared Task (2021)
• FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information (2021) — 259 citations
• Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking (2020) — 298 citations
• How Do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking (2020) — 104 citations