Peter F. Schulam
Artificial Intelligence · 2015–2018
A researcher in the Science corpus: 8 papers; trusts 64 researchers and is trusted by 31 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 87 citations into it; #10,154 of 35,345 by trust here, #15,542 by citations
- Statistics and Probability — 36 citations into it
- Causal Inference & Discovery — 23 citations into it; #107 of 364 by trust here, #151 by citations
- Epidemiology — 20 citations into it
- Medical & Health AI — 19 citations into it; #28 of 239 by trust here, #141 by citations
- Probabilistic & Bayesian Methods — 17 citations into it
- Representation & Self-Supervised Learning — 17 citations into it; #2,979 of 3,145 by trust here, #2,516 by citations
- Radiology, Nuclear Medicine and Imaging — 16 citations into it
Whom Peter F. Schulam trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Donald B. Rubin
- James M. Robins
- Mohammed Saeed
- J. Lok
- Judea Pearl
- Elja Arjas
- Suchi Saria
- Bernhard Schölkopf
- Kirill Dyagilev
- Kevin P. Murphy
Who trusts Peter F. Schulam overall
- Suchi Saria
- Bryan Lim
- Ioana Bica
- Adarsh Subbaswamy
- Hossein Soleimani
- Li-Fang Cheng
- Fei Wang
- Michael Oberst
- Yanbo Xu
- Ahmed M. Alaa
Papers from Peter F. Schulam's perspective
Ordered by how strongly the researchers Peter F. Schulam trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- GENERATIVE ADVERSARIAL NETS
- Reinforcement Learning: An Introduction
Their papers
- A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure (2015) — 104 citations
- Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery (2015) — 116 citations
- What-If Reasoning with Counterfactual Gaussian Processes (2017) — 8 citations
- Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport (2018) — 163 citations
- A Review of Challenges and Opportunities in Machine Learning for Health. (2018) — 398 citations
- Opportunities in Machine Learning for Healthcare (2018) — 112 citations
- Reliable Decision Support using Counterfactual Models (2017) — 227 citations
- A Probabilistic Graphical Model for Individualizing Prognosis in Chronic, Complex Diseases (2015)