Randall C. Wetzel

Artificial Intelligence · 2012–2016

A researcher in the Science corpus: 6 papers; trusts 58 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #6,824 of 35,345 by trust here, #6,720 by citations
• Computer Vision and Pattern Recognition — 31 citations into it
• Model Architectures — 28 citations into it; #762 of 2,278 by trust here, #810 by citations
• Molecular Biology — 19 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Natural Language Processing — 13 citations into it
• Medical & Health AI — 11 citations into it; #23 of 239 by trust here, #32 by citations
• Signal Processing — 10 citations into it; #1,060 of 7,572 by trust here, #716 by citations

Whom Randall C. Wetzel trusts overall

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

1. Zachary Chase Lipton
2. Felix Alexander Gers
3. J. Urgen Schmidhuber
4. Paul J. Werbos
5. Sepp Hochreiter
6. Geoffrey E. Hinton
7. Alex Graves
8. Yoshua Bengio
9. David C. Kale
10. Benjamin M. Marlin

Who trusts Randall C. Wetzel overall

1. Zachary Chase Lipton
2. David C. Kale
3. Edward Choi
4. Hossein Soleimani
5. Zhengping Che
6. Joseph D. Futoma
7. Huan-Zhi Song
8. Hrayr Harutyunyan
9. Xiang Gao
10. Cao Xiao

Papers from Randall C. Wetzel's perspective

Ordered by how strongly the researchers Randall C. Wetzel trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Learning to Diagnose with LSTM Recurrent Neural Networks (2015) — 1,224 citations
• Unsupervised pattern discovery in electronic health care data using probabilistic clustering models (2012) — 186 citations
• Modeling Missing Data in Clinical Time Series with RNNs (2016)
• Directly Modeling Missing Data in Sequences with RNNs: Improved Classification of Clinical Time Series (2016) — 273 citations
• Causal Phenotype Discovery via Deep Networks (2015) — 36 citations
• Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks (2015) — 39 citations