Edward Choi

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 13 papers; trusts 387 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 266 citations into it; #7,522 of 35,345 by trust here, #3,204 by citations
• Computer Vision and Pattern Recognition — 115 citations into it; #20,209 of 21,575 by trust here, #6,371 by citations
• Model Architectures — 57 citations into it; #1,425 of 2,278 by trust here, #950 by citations
• Natural Language Processing — 53 citations into it
• Representation & Self-Supervised Learning — 50 citations into it
• Molecular Biology — 44 citations into it
• Generative Models — 38 citations into it; #1,851 of 1,956 by trust here, #989 by citations
• Signal Processing — 38 citations into it

Whom Edward Choi trusts overall

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

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

Who trusts Edward Choi overall

1. Cao Xiao
2. Junyuan Shang
3. Jimeng Sun
4. Wei Zhang
5. Bryan Lim
6. Fei Wang
7. Yuan Ling
8. Yao Ming
9. Joseph D. Futoma
10. Cecilia Panigutti

Papers from Edward Choi's perspective

Ordered by how strongly the researchers Edward Choi 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks (2017) — 362 citations
• Analyzing the role of model uncertainty for electronic health records (2019) — 123 citations
• RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism (2016) — 1,544 citations
• GRAM: Graph-based Attention Model for Healthcare Representation Learning (2016) — 770 citations
• Multi-layer Representation Learning for Medical Concepts (2016) — 540 citations
• Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer (2019) — 301 citations
• Causal Regularization (2019) — 57 citations
• Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review (2018) — 716 citations
• Causal Regularization (2017)
• 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
• Using recurrent neural network models for early detection of heart failure onset (2016) — 825 citations
• Doctor AI: Predicting Clinical Events via Recurrent Neural Networks (2015)