Walter F. Stewart

Artificial Intelligence · 2010–2019

A researcher in the Science corpus: 14 papers; trusts 266 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 165 citations into it; #7,075 of 35,345 by trust here, #3,703 by citations
• Computer Vision and Pattern Recognition — 89 citations into it; #20,212 of 21,575 by trust here, #11,286 by citations
• Natural Language Processing — 39 citations into it
• Generative Models — 36 citations into it; #1,849 of 1,956 by trust here, #989 by citations
• Model Architectures — 36 citations into it; #1,368 of 2,278 by trust here, #950 by citations
• Representation & Self-Supervised Learning — 36 citations into it
• Signal Processing — 33 citations into it
• Molecular Biology — 26 citations into it

Whom Walter F. Stewart trusts overall

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

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

Who trusts Walter F. Stewart overall

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

Papers from Walter F. Stewart's perspective

Ordered by how strongly the researchers Walter F. Stewart 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• 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
• Causal Regularization (2019) — 57 citations
• SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping (2018) — 589 citations
• LEAP: Learning to Prescribe Effective and Safe Treatment Combinations for Multimorbidity (2017) — 260 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
• Early Detection of Heart Failure with Varying Prediction Windows by Structured and Unstructured Data in Electronic Health Records (2015) — 58 citations
• Doctor AI: Predicting Clinical Events via Recurrent Neural Networks (2015)
• Limestone: High-throughput candidate phenotype generation via tensor factorization (2014) — 164 citations
• Combining Knowledge and Data Driven Insights for Identifying Risk Factors using Electronic Health Records (2012) — 98 citations
• Prediction Modeling Using EHR Data: Challenges, Strategies, and a Comparison of Machine Learning Approaches (2010) — 361 citations