Philip Arthur

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 4 papers; trusts 134 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 73 citations into it; #13,509 of 35,345 by trust here, #11,921 by citations
• Natural Language Processing — 66 citations into it; #3,319 of 5,688 by trust here, #3,053 by citations
• Computer Vision and Pattern Recognition — 34 citations into it; #9,224 of 21,575 by trust here, #8,612 by citations
• Model Architectures — 16 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Signal Processing — 10 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Large Language Models — 5 citations into it

Whom Philip Arthur trusts overall

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

1. Yoshua Bengio
2. Philipp Koehn
3. Ilya Sutskever
4. Kyunghyun Cho
5. Dzmitry Bahdanau
6. Geoffrey E. Hinton
7. Diederik P. Kingma
8. Oriol Vinyals
9. Franz Joseph Och
10. Alex Krizhevsky

Who trusts Philip Arthur overall

1. Felix Stahlberg
2. Toan Q. Nguyen
3. Graham Neubig
4. Jan Niehues
5. Sarthak Garg
6. Heng Yu
7. Jian Zhang
8. Jingyi Zhang
9. David Chiang
10. Raj Dabre

Papers from Philip Arthur's perspective

Ordered by how strongly the researchers Philip Arthur 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Linguistic Unit Discovery from Multi-Modal Inputs in Unwritten Languages: Summary of the “Speaking Rosetta” JSALT 2017 Workshop (2018) — 34 citations
• Incorporating Discrete Translation Lexicons into Neural Machine Translation (2016) — 217 citations
• Multilingual Neural Machine Translation With Soft Decoupled Encoding (2019) — 66 citations
• Neural Machine Translation via Binary Code Prediction (2017) — 17 citations