Joseph D. Futoma

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 8 papers; trusts 112 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it; #27,195 of 35,345 by trust here, #15,236 by citations
• Reinforcement Learning — 53 citations into it
• Epidemiology — 32 citations into it; #1,197 of 2,148 by trust here, #1,080 by citations
• Medical & Health AI — 27 citations into it; #161 of 239 by trust here, #159 by citations
• Health Informatics — 18 citations into it
• Management Science and Operations Research — 15 citations into it
• Surgery — 14 citations into it
• Economics and Econometrics — 13 citations into it

Whom Joseph D. Futoma trusts overall

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

1. Diederik P. Kingma
2. Richard S. Sutton
3. J. Urgen Schmidhuber
4. Sepp Hochreiter
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Jimmy Ba
8. Ilya Sutskever
9. Alex Krizhevsky
10. Andrew Barto

Who trusts Joseph D. Futoma overall

1. Mark P. Sendak
2. Yulia Rubanova
3. Chun-Hao Kingsley Chang
4. Edward De Brouwer
5. Hussein Mozannar
6. Chao Yu
7. Madeleine Clare Elish
8. Ricky T. Q. Chen
9. Mingjie Mai
10. Jaak Simm

Papers from Joseph D. Futoma's perspective

Ordered by how strongly the researchers Joseph D. Futoma 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. 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs (2020) — 63 citations
• POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning (2020) — 62 citations
• The myth of generalisability in clinical research and machine learning in health care (2020) — 366 citations
• An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection (2017) — 142 citations
• Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions (2020) — 69 citations
• "The human body is a black box": supporting clinical decision-making with deep learning (2019) — 220 citations
• Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks (2018) — 22 citations
• Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier (2017) — 194 citations