J. Rohrer

Statistics and Probability · 2017–2020

A researcher in the Science corpus: 2 papers; trusts 23 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 36 citations into it; #3,616 of 12,932 by trust here, #7,914 by citations
• Experimental and Cognitive Psychology — 13 citations into it
• Sociology and Political Science — 10 citations into it
• Applied Psychology — 8 citations into it
• Economics and Econometrics — 8 citations into it
• Artificial Intelligence — 6 citations into it; #10,317 of 35,345 by trust here, #26,682 by citations
• Social Psychology — 5 citations into it

Whom J. Rohrer trusts overall

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

1. Judea Pearl
2. Eric Turkheimer
3. P. Rozin
4. F. Thoemmes
5. Felix Elwert
6. Max Welling
7. Donald B. Rubin
8. Sander Greenland
9. Christopher Winship
10. Karthika Mohan

Who trusts J. Rohrer overall

1. Richard York
2. Markku V. J. Maula
3. Friday Osemenshan Anetor
4. W. Stam
5. Ebes Esho
6. G. Verhoef
7. Tal Yarkoni

Papers from J. Rohrer's perspective

Ordered by how strongly the researchers J. Rohrer 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. ImageNet: A large-scale hierarchical image database
4. Learning Multiple Layers of Features from Tiny Images
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data (2017) — 829 citations
• Replicability, Robustness, and Reproducibility in Psychological Science. (2020) — 543 citations