Scott Roy

Artificial Intelligence · with Caswell · 2008–2019

A researcher in the Science corpus: 4 papers; trusts 93 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it; #21,746 of 35,345 by trust here, #16,166 by citations
• Natural Language Processing — 80 citations into it; #4,562 of 5,688 by trust here, #3,826 by citations
• Computer Vision and Pattern Recognition — 38 citations into it
• Model Architectures — 18 citations into it
• Large Language Models — 9 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Machine Translation — 6 citations into it; #123 of 292 by trust here, #150 by citations
• Molecular Biology — 5 citations into it

Whom Scott Roy trusts overall

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

1. Rico Sennrich
2. Yoshua Bengio
3. Alex Graves
4. Geoffrey E. Hinton
5. Yann LeCun
6. Tomas Mikolov
7. Matt Post
8. Ilya Sutskever
9. Diederik P. Kingma
10. Kyunghyun Cho

Who trusts Scott Roy overall

1. Markus Freitag

Papers from Scott Roy's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
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

• Contextual LSTM (CLSTM) models for Large scale NLP tasks (2016) — 229 citations
• APE at Scale and Its Implications on MT Evaluation Biases (2019) — 69 citations
• Text Repair Model for Neural Machine Translation (2019) — 8 citations
• Online Models for Content Optimization (2008) — 145 citations