Michael Oberst

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 24 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 10 citations into it
• Causal Inference & Discovery — 7 citations into it
• Reinforcement Learning — 7 citations into it
• Medical & Health AI — 5 citations into it

Whom Michael Oberst trusts overall

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

1. Judea Pearl
2. Florian Heiss
3. Richard S. Sutton
4. Jan Peters
5. Chris J. Maddison
6. Peter F. Schulam
7. V. Aguirregabiria
8. Peter M. Bentler
9. Fredrik D. Johansson
10. Arthur Guez

Who trusts Michael Oberst overall

1. Joseph D. Futoma
2. Cameron Voloshin
3. Yura N. Perov
4. Michael C. Hughes
5. Hoang Minh Le
6. Finale Doshi-Velez
7. Logan Graham
8. Nan Jiang
9. Nick Pawlowski

Papers from Michael Oberst's perspective

Ordered by how strongly the researchers Michael Oberst 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Reinforcement Learning: An Introduction
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet: A large-scale hierarchical image database

Their papers

• Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models (2019) — 200 citations