Lauren Hannah

Statistics and Probability · 2009–2014

A researcher in the Science corpus: 4 papers; trusts 78 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 49 citations into it; #3,365 of 12,932 by trust here, #4,517 by citations
• Artificial Intelligence — 43 citations into it; #9,715 of 35,345 by trust here, #18,586 by citations
• Probabilistic & Bayesian Methods — 20 citations into it; #986 of 1,525 by trust here, #1,274 by citations
• Management Science and Operations Research — 12 citations into it
• Economics and Econometrics — 10 citations into it
• Finance — 9 citations into it
• Statistical Learning Theory — 9 citations into it
• Reinforcement Learning — 9 citations into it

Whom Lauren Hannah trusts overall

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

1. Radford M. Neal
2. Niko Speybroeck
3. Mary C. Meyer
4. Madeleine Cule
5. Jerome H. Friedman
6. Michael I. Jordan
7. Carl E. Rasmussen
8. Leo Breiman
9. Melanie Birke
10. Jianqing Fan

Who trusts Lauren Hannah overall

1. Yining Chen
2. Samuel J. Gershman
3. Maria DeYoreo
4. Gábor Balázs
5. Hao Wang
6. Edwin Fong
7. Seyed Hamid Rezatofighi
8. David M. Blei
9. Prem Gopalan
10. Richard J. Samworth

Papers from Lauren Hannah's perspective

Ordered by how strongly the researchers Lauren Hannah trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Dirichlet Process Mixtures of Generalized Linear Models (2009) — 202 citations
• Beta-Negative Binomial Process and Poisson Factor Analysis (2011) — 257 citations
• Multivariate convex regression with adaptive partitioning (2011) — 137 citations
• Semiconvex Regression for Metamodeling-Based Optimization (2014) — 11 citations