Dale R. Durran
Atmospheric Science · 2000–2020
A researcher in the Science corpus: 8 papers; trusts 25 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Atmospheric Science — 16 citations into it; #1,667 of 4,804 by trust here, #1,505 by citations
- Artificial Intelligence — 14 citations into it
- Global and Planetary Change — 14 citations into it; #4,156 of 10,225 by trust here, #4,014 by citations
- Model Architectures — 10 citations into it
- Environmental Engineering — 9 citations into it
- Computer Vision and Pattern Recognition — 8 citations into it
- Electrical and Electronic Engineering — 6 citations into it
- Representation & Self-Supervised Learning — 5 citations into it
Whom Dale R. Durran trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- William C. Skamarock
- Louis J. Wicker
- Diederik P. Kingma
- Christopher A. Kennedy
- Peter Dueben
- Stephan Rasp
- Jimmy Ba
- Noah D. Brenowitz
- Sepp Hochreiter
- Mark H. Carpenter
Who trusts Dale R. Durran overall
- Christiane Jablonowski
- Roberto Buizza
- Klaus Werner
- David L. Williamson
- Peter N. Blossey
- S. Vadas
- Martin Leutbecher
- Stephan Rasp
- Iurii Karpenko
- Dave Fritts
Papers from Dale R. Durran's perspective
Ordered by how strongly the researchers Dale R. Durran trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere (2020) — 340 citations
- Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500‐hPa Geopotential Height From Historical Weather Data (2019) — 315 citations
- Atmospheric Predictability: Why Butterflies Are Not of Practical Importance (2014) — 138 citations
- Implicit–Explicit Multistep Methods for Fast-Wave–Slow-Wave Problems (2012) — 79 citations
- Numerical Methods for Fluid Dynamics (2010) — 522 citations
- Estimating Topographic Blocking Using a Froude Number When the Static Stability Is Nonuniform (2008) — 98 citations
- The Dynamics of Mountain-Wave-Induced Rotors (2002) — 209 citations
- A Numerical Study of Three-Dimensional Gravity Waves Triggered by Deep Tropical Convection and Their Role in the Dynamics of the QBO (2000) — 226 citations