Tyler Derr

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 5 papers; trusts 145 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 131 citations into it; #27,619 of 35,345 by trust here, #13,760 by citations
• Natural Language Processing — 61 citations into it; #5,143 of 5,688 by trust here, #4,250 by citations
• Computer Vision and Pattern Recognition — 45 citations into it; #19,735 of 21,575 by trust here, #13,269 by citations
• Safety, Robustness & Interpretability — 32 citations into it; #2,018 of 2,251 by trust here, #1,857 by citations
• Graph Learning — 24 citations into it
• Statistical and Nonlinear Physics — 16 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Molecular Biology — 11 citations into it

Whom Tyler Derr trusts overall

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

1. Ronald J. Williams
2. Yoshua Bengio
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Ilya Sutskever
6. Oriol Vinyals
7. Yann LeCun
8. Alex Krizhevsky
9. Jure Leskovec
10. Sepp Hochreiter

Who trusts Tyler Derr overall

1. Aneeshan Sain
2. Jie Zhou
3. Emily Dinan
4. John Pougué-Biyong
5. Marinka Zitnik
6. Haochen Liu
7. Ayan Kumar Bhunia
8. Ganqu Cui
9. Matthias Fey
10. Gavin Abercrombie

Papers from Tyler Derr's perspective

Ordered by how strongly the researchers Tyler Derr 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. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Chat as Expected: Learning to Manipulate Black-box Neural Dialogue Models (2020) — 15 citations
• Knowledge Graph Prompting for Multi-Document Question Answering (2023) — 329 citations
• Node Similarity Preserving Graph Convolutional Networks (2020) — 328 citations
• Say What I Want: Towards the Dark Side of Neural Dialogue Models (2019) — 17 citations
• Signed Graph Convolutional Networks (2018) — 325 citations