David C. Kale

Artificial Intelligence · 2012–2019

A researcher in the Science corpus: 10 papers; trusts 105 researchers and is trusted by 56 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 105 citations into it; #4,266 of 35,345 by trust here, #3,895 by citations
• Computer Vision and Pattern Recognition — 48 citations into it
• Model Architectures — 35 citations into it; #769 of 2,278 by trust here, #810 by citations
• Representation & Self-Supervised Learning — 29 citations into it
• Natural Language Processing — 26 citations into it
• Molecular Biology — 25 citations into it; #9,298 of 17,773 by trust here, #9,367 by citations
• Epidemiology — 21 citations into it; #815 of 2,148 by trust here, #1,165 by citations
• Medical & Health AI — 21 citations into it; #24 of 239 by trust here, #32 by citations

Whom David C. Kale 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. Geoffrey E. Hinton
5. Yoshua Bengio
6. Sepp Hochreiter
7. Alex Graves
8. Paul J. Werbos
9. Ilya Sutskever
10. Alex Krizhevsky

Who trusts David C. Kale overall

1. Zachary Chase Lipton
2. Edward Choi
3. Zhengping Che
4. Randall C. Wetzel
5. Sanjay Purushotham
6. Huan-Zhi Song
7. Zexian Zeng
8. Hrayr Harutyunyan
9. Hossein Soleimani
10. Cao Xiao

Papers from David C. Kale's perspective

Ordered by how strongly the researchers David C. Kale 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. Long Short-Term Memory
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. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning to Diagnose with LSTM Recurrent Neural Networks (2015) — 1,224 citations
• Multitask learning and benchmarking with clinical time series data (2017) — 1,081 citations
• Deep Computational Phenotyping (2015) — 267 citations
• Unsupervised pattern discovery in electronic health care data using probabilistic clustering models (2012) — 186 citations
• Do no harm: a roadmap for responsible machine learning for health care (2019) — 925 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
• Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge (2016) — 191 citations
• Causal Phenotype Discovery via Deep Networks (2015) — 36 citations
• Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks (2015) — 39 citations