Raphael Shu

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 2 papers; trusts 126 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 40 citations into it; #24,084 of 35,345 by trust here, #32,362 by citations
• Natural Language Processing — 39 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it; #14,888 of 21,575 by trust here, #20,271 by citations
• Evaluation & Benchmarks — 14 citations into it
• Model Architectures — 6 citations into it
• Machine Translation — 5 citations into it; #178 of 292 by trust here, #287 by citations

Whom Raphael Shu trusts overall

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

1. Ilya Sutskever
2. Diederik P. Kingma
3. Franz Joseph Och
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Chin-Yew Lin
7. Dzmitry Bahdanau
8. Alex Graves
9. Oriol Vinyals
10. Richard Socher

Who trusts Raphael Shu overall

1. Sainbayar Sukhbaatar
2. Edouard Grave
3. Piotr Bojanowski

Papers from Raphael Shu's perspective

Ordered by how strongly the researchers Raphael Shu 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. 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

• An Empirical Study of Adequate Vision Span for Attention-Based Neural Machine Translation (2016) — 7 citations
• Reward Optimization for Neural Machine Translation with Learned Metrics (2021) — 15 citations