David Robinson

Artificial Intelligence · with Tepper · 2018

A researcher in the Science corpus: 2 papers; trusts 32 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it; #11,015 of 35,345 by trust here, #15,542 by citations
• Natural Language Processing — 20 citations into it; #2,548 of 5,688 by trust here, #3,369 by citations
• Information Systems — 12 citations into it
• Safety, Robustness & Interpretability — 10 citations into it
• Communication — 6 citations into it; #802 of 3,460 by trust here, #1,198 by citations
• AI Ethics, Fairness & Society — 5 citations into it

Whom David Robinson trusts overall

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

1. Zeerak Talat
2. P. Burnap
3. Ji Ho Park
4. Thomas Davidson
5. Anna Schmidt
6. Ziqi Zhang
7. Dirk Hovy
8. Matthew Leighton Williams
9. Pascale Fung
10. Diederik P. Kingma

Who trusts David Robinson overall

1. Betty van Aken
2. Mladen Karan
3. Ziqi Zhang
4. Julian Risch
5. Bertie Vidgen
6. Frederick Liu
7. Rui Cao
8. Marzieh Mozafari
9. Rohan Kshirsagar
10. Ralf Krestel

Papers from David Robinson's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Attention is All you Need (Neural Information Processing Systems)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
6. Long Short-Term Memory
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Efficient Estimation of Word Representations in Vector Space

Their papers

• Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network (2018) — 653 citations
• Hate Speech Detection on Twitter: Feature Engineering v.s. Feature Selection (2018) — 31 citations