Verna Dankers

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 40 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it; #24,027 of 35,345 by trust here, #20,377 by citations
• Natural Language Processing — 63 citations into it
• Model Architectures — 38 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Computational Linguistics & Semantics — 17 citations into it
• Representation & Self-Supervised Learning — 16 citations into it; #2,379 of 3,145 by trust here, #2,355 by citations
• Computational Theory and Mathematics — 5 citations into it

Whom Verna Dankers trusts overall

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

1. Ilya Sutskever
2. Alex Graves
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yoshua Bengio
6. Jacob Andreas
7. Oriol Vinyals
8. Alex Krizhevsky
9. Yann LeCun
10. Ashish Vaswani

Who trusts Verna Dankers overall

1. Ofir Press
2. Zhengzhong Liang
3. R. Thomas McCoy
4. Benjamin Newman

Papers from Verna Dankers's perspective

Ordered by how strongly the researchers Verna Dankers trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Compositionality Decomposed: How do Neural Networks Generalise? (2019) — 408 citations
• Compositionality Decomposed: How do Neural Networks Generalise? (Extended Abstract) (2020) — 29 citations
• Transcoding Compositionally: Using Attention to Find More Generalizable Solutions (2019) — 30 citations