Onur Atan

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 35 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 40 citations into it; #16,986 of 35,345 by trust here, #25,809 by citations
• Statistics and Probability — 22 citations into it; #6,171 of 12,932 by trust here, #7,508 by citations
• Management Science and Operations Research — 21 citations into it
• Reinforcement Learning — 17 citations into it
• Causal Inference & Discovery — 13 citations into it; #309 of 364 by trust here, #343 by citations
• Representation & Self-Supervised Learning — 12 citations into it
• Statistical Learning Theory — 7 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom Onur Atan trusts overall

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

1. Nan Jiang
2. Diederik P. Kingma
3. Lihong Li
4. Adith Swaminathan
5. Jennifer L. Hill
6. Thorsten Joachims
7. R. Prentice
8. J. Langford
9. Yoshua Bengio
10. Ronald J. Williams

Who trusts Onur Atan overall

1. Liuyi Yao
2. Ahmed M. Alaa
3. Ioana Bica
4. Mihaela van der Schaar
5. Zhixuan Chu
6. Sheng Li
7. Bryan Lim
8. Bernhard Schölkopf
9. James Jordon
10. Yaliang Li

Papers from Onur Atan's perspective

Ordered by how strongly the researchers Onur Atan 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Deep-Treat: Learning Optimal Personalized Treatments From Observational Data Using Neural Networks (2018) — 76 citations
• Learning Optimal Policies from Observational Data (2018) — 20 citations
• Constructing effective personalized policies using counterfactual inference from biased data sets with many features (2016) — 13 citations