Matthew B. A. McDermott

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 6 papers; trusts 87 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 119 citations into it; #33,713 of 35,345 by trust here, #7,728 by citations
• Computer Vision and Pattern Recognition — 55 citations into it
• Safety, Robustness & Interpretability — 43 citations into it
• Natural Language Processing — 41 citations into it; #4,102 of 5,688 by trust here, #1,667 by citations
• Radiology, Nuclear Medicine and Imaging — 31 citations into it
• Evaluation & Benchmarks — 23 citations into it
• Health Informatics — 21 citations into it; #163 of 240 by trust here, #50 by citations
• Pulmonary and Respiratory Medicine — 17 citations into it

Whom Matthew B. A. McDermott trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Ilya Sutskever
6. Sepp Hochreiter
7. Kaiming He
8. Jimmy Ba
9. Yann LeCun
10. Alex Graves

Who trusts Matthew B. A. McDermott overall

1. Irene Y. Chen
2. Laleh Seyyed-Kalantari
3. Zhihong Chen

Papers from Matthew B. A. McDermott's perspective

Ordered by how strongly the researchers Matthew B. A. McDermott 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Going deeper with convolutions

Their papers

• Publicly Available Clinical BERT Embeddings (2019) — 2,728 citations
• Clinically Accurate Chest X-Ray Report Generation (2019) — 353 citations
• Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations (2021) — 830 citations
• CheXclusion: Fairness gaps in deep chest X-ray classifiers (2020) — 385 citations
• Hurtful words: quantifying biases in clinical contextual word embeddings (2020) — 206 citations
• Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks (2019) — 131 citations