Iain James Marshall

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 4 papers; trusts 32 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #8,320 of 35,345 by trust here, #10,667 by citations
• Natural Language Processing — 32 citations into it; #2,261 of 5,688 by trust here, #2,930 by citations
• Computer Vision and Pattern Recognition — 7 citations into it
• Model Architectures — 7 citations into it; #920 of 2,278 by trust here, #1,530 by citations
• Representation & Self-Supervised Learning — 7 citations into it
• Statistics and Probability — 7 citations into it; #10,424 of 12,932 by trust here, #7,914 by citations
• Evaluation & Benchmarks — 6 citations into it

Whom Iain James Marshall trusts overall

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

1. Radford M. Neal
2. Andrew Y. Ng
3. Leo Breiman
4. Geoffrey E. Hinton
5. Byron C. Wallace
6. Thorsten Joachims
7. Burr Settles
8. Thomas G. Dietterich
9. Alex Krizhevsky
10. Ye Zhang

Who trusts Iain James Marshall overall

1. Jay DeYoung
2. David Wadden
3. Eric P. Lehman
4. Ye Zhang
5. Byron C. Wallace
6. Peter Hase
7. Iz Beltagy
8. Madeleine van Zuylen
9. Lucy Lu Wang
10. Matthew J. Page

Papers from Iain James Marshall's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Rationale-Augmented Convolutional Neural Networks for Text Classification (2016) — 172 citations
• A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature (2018) — 254 citations
• Machine learning for identifying Randomized Controlled Trials: An evaluation and practitioner's guide (2018) — 368 citations
• Toward systematic review automation: a practical guide to using machine learning tools in research synthesis (2019) — 491 citations