Gretchen G. Moisen

Statistics and Probability · 2002–2017

A researcher in the Science corpus: 9 papers; trusts 47 researchers and is trusted by 69 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 41 citations into it
• Ecology — 37 citations into it; #3,211 of 10,717 by trust here, #4,784 by citations
• Ecological Modeling — 34 citations into it; #233 of 2,530 by trust here, #603 by citations
• Nature and Landscape Conservation — 30 citations into it; #197 of 6,941 by trust here, #881 by citations
• Artificial Intelligence — 19 citations into it; #10,715 of 35,345 by trust here, #25,809 by citations
• Environmental Engineering — 11 citations into it; #243 of 5,479 by trust here, #519 by citations
• Statistical Learning Theory — 11 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom Gretchen G. Moisen trusts overall

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

1. Jerome H. Friedman
2. Antoine Guisan
3. Yann LeCun
4. Trevor J. Hastie
5. Michael P. Austin
6. Leo Breiman
7. Robert Tibshirani
8. Elizabeth R. DeLong
9. M. Greiner
10. Gordon K. Smyth

Who trusts Gretchen G. Moisen overall

1. Michael P. Austin
2. Wilfried Thuiller
3. B. Schr
4. Jane Elith
5. Elizabeth A. Freeman
6. J. Leathwick
7. Andreas Hamann
8. Björn Reineking
9. Antoine Guisan
10. Ente Rood

Papers from Gretchen G. Moisen's perspective

Ordered by how strongly the researchers Gretchen G. Moisen 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. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Regression Shrinkage and Selection via the Lasso
10. Going deeper with convolutions

Their papers

• How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms (2017) — 193 citations
• Model-Assisted Survey Regression Estimation with the Lasso (2017) — 78 citations
• Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance (2016) — 203 citations
• A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and Kappa (2008) — 616 citations
• PresenceAbsence: An R Package for Presence Absence Analysis (2008) — 514 citations
• Model-Assisted Estimation of Forest Resources With Generalized Additive Models (2007) — 126 citations
• Effects of sample survey design on the accuracy of classification tree models in species distribution models (2006) — 150 citations
• Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods (2006) — 299 citations
• Comparing five modelling techniques for predicting forest characteristics (2002) — 486 citations