Jonathan A. Weyn
Atmospheric Science · 2019–2020
A researcher in the Science corpus: 3 papers; trusts 25 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Atmospheric Science — 31 citations into it
- Global and Planetary Change — 27 citations into it
- Artificial Intelligence — 24 citations into it
- Computer Vision and Pattern Recognition — 22 citations into it
- Model Architectures — 19 citations into it
- Environmental Engineering — 13 citations into it
- Optimization & Training — 8 citations into it
- Electrical and Electronic Engineering — 7 citations into it
Whom Jonathan A. Weyn trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Peter Dueben
- Stephan Rasp
- Sepp Hochreiter
- Jimmy Ba
- Noah D. Brenowitz
- Olaf Ronneberger
- Peter Bauer
- J. Urgen Schmidhuber
- L. Harris
Who trusts Jonathan A. Weyn overall
Papers from Jonathan A. Weyn's perspective
Ordered by how strongly the researchers Jonathan A. Weyn 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)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- 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
- WeatherBench: A Benchmark Data Set for Data‐Driven Weather Forecasting (2020) — 725 citations
- Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500‐hPa Geopotential Height From Historical Weather Data (2019) — 315 citations