Jon D. Duke

Artificial Intelligence · 2015–2018

A researcher in the Science corpus: 4 papers; trusts 49 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #25,804 of 35,345 by trust here, #12,580 by citations
• Computer Vision and Pattern Recognition — 36 citations into it; #20,210 of 21,575 by trust here, #11,286 by citations
• Generative Models — 33 citations into it; #1,846 of 1,956 by trust here, #989 by citations
• Natural Language Processing — 16 citations into it
• Optimization & Training — 11 citations into it
• Epidemiology — 7 citations into it
• Safety, Robustness & Interpretability — 6 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Jon D. Duke trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ronald J. Williams
5. Ian J. Goodfellow
6. Alex Krizhevsky
7. Jimmy Ba
8. Kaiming He
9. Yann LeCun
10. Ilya Sutskever

Who trusts Jon D. Duke overall

1. Ilias Chalkidis
2. Cao Xiao

Papers from Jon D. Duke's perspective

Ordered by how strongly the researchers Jon D. Duke 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Explainable Prediction of Medical Codes from Clinical Text (2018) — 692 citations
• Generating Multi-label Discrete Patient Records using Generative Adversarial Networks (2017) — 725 citations
• Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks (2017) — 78 citations
• Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers (2015) — 1,262 citations