P. Richard Hahn
Artificial Intelligence · 2016–2019
A researcher in the Science corpus: 5 papers; trusts 71 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 98 citations into it
- Causal Inference & Discovery — 85 citations into it
- Statistics and Probability — 79 citations into it
- Probabilistic & Bayesian Methods — 22 citations into it
- Signal Processing — 13 citations into it
- Management Science and Operations Research — 12 citations into it
- Molecular Biology — 11 citations into it
- Computer Networks and Communications — 10 citations into it
Whom P. Richard Hahn trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Donald B. Rubin
- Judea Pearl
- Paul R. Rosenbaum
- Jan Peters
- Peter Spirtes
- Guido W. Imbens
- Aapo Hyvärinen
- Geoffrey E. Hinton
- Robert Tibshirani
- Carlos M. Carvalho
Who trusts P. Richard Hahn overall
Papers from P. Richard Hahn's perspective
Ordered by how strongly the researchers P. Richard Hahn 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
- GENERATIVE ADVERSARIAL NETS
- Attention is All you Need (Neural Information Processing Systems)
- Regression Shrinkage and Selection via the Lasso
- Learning Multiple Layers of Features from Tiny Images
- Auto-Encoding Variational Bayes (International Conference on Learning Representations)
Their papers
- Atlantic Causal Inference Conference (ACIC) Data Analysis Challenge 2017 (2019) — 52 citations
- Regularization and confounding in linear regression for treatment effect estimation (2016) — 97 citations
- A national experiment reveals where a growth mindset improves achievement (2019) — 1,172 citations
- A Survey of Learning Causality with Data (2018) — 184 citations
- A Survey of Learning Causality with Data: Problems and Methods (2018) — 335 citations