Richard F. E. Sutcliffe

Artificial Intelligence · 2013–2020

A researcher in the Science corpus: 2 papers; trusts 36 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it; #27,464 of 35,345 by trust here, #26,682 by citations
• Natural Language Processing — 20 citations into it; #4,514 of 5,688 by trust here, #4,793 by citations
• Evaluation & Benchmarks — 11 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it; #17,064 of 21,575 by trust here, #20,271 by citations
• Large Language Models — 5 citations into it
• Representation & Self-Supervised Learning — 5 citations into it; #2,595 of 3,145 by trust here, #3,047 by citations

Whom Richard F. E. Sutcliffe trusts overall

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

1. Diederik P. Kingma
2. Alec Radford
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Jimmy Ba
6. Richard Socher
7. Ilya Sutskever
8. Alex Krizhevsky
9. Jacob Devlin
10. Alex Wang

Who trusts Richard F. E. Sutcliffe overall

1. Kevin Gimpel
2. Karen Livescu
3. Freda Shi

Papers from Richard F. E. Sutcliffe's perspective

Ordered by how strongly the researchers Richard F. E. Sutcliffe trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation (2013) — 49 citations
• Textual Data Augmentation for Efficient Active Learning on Tiny Datasets (2020) — 50 citations