Sebastian Lerch

Global and Planetary Change · 2013–2018

A researcher in the Science corpus: 3 papers; trusts 52 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Global and Planetary Change — 39 citations into it; #5,603 of 10,225 by trust here, #9,422 by citations
• Atmospheric Science — 35 citations into it; #589 of 4,804 by trust here, #2,465 by citations
• Environmental Engineering — 16 citations into it; #1,071 of 5,479 by trust here, #2,867 by citations
• Artificial Intelligence — 15 citations into it; #24,533 of 35,345 by trust here, #32,362 by citations
• Statistics and Probability — 12 citations into it
• Management Science and Operations Research — 9 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Finance — 6 citations into it

Whom Sebastian Lerch trusts overall

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

1. Tilmann Gneiting
2. Michael Scheuerer
3. Adrian E. Raftery
4. Petra Friederichs
5. Thomas M. Hamill
6. Leo Breiman
7. Thordis Linda Thorarinsdottir
8. S. Bentzien
9. D. Wilks
10. Veronica J. Berrocal

Who trusts Sebastian Lerch overall

1. Kira Feldmann
2. Federico Bassetti
3. Stephan Rasp
4. Manuel Gebetsberger
5. Jonathan A. Weyn
6. Michael Scheuerer
7. Roberto Casarin
8. Jakob W. Messner
9. Martin G. Schultz
10. Thordis Linda Thorarinsdottir

Papers from Sebastian Lerch's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Strictly Proper Scoring Rules, Prediction, and Estimation
6. Using Bayesian Model Averaging to Calibrate Forecast Ensembles
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Comparison of non-homogeneous regression models for probabilistic wind speed forecasting (2013) — 103 citations
• Neural networks for post-processing ensemble weather forecasts (2018) — 440 citations
• Evaluating Probabilistic Forecasts with scoringRules (2017) — 295 citations