Pierre Gentine
Global and Planetary Change · 2018–2020
A researcher in the Science corpus: 9 papers; trusts 12 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Global and Planetary Change — 206 citations into it; #3,375 of 10,225 by trust here, #2,355 by citations
- Atmospheric Science — 148 citations into it; #2,044 of 4,804 by trust here, #1,196 by citations
- Artificial Intelligence — 21 citations into it
- Oceanography — 18 citations into it
- Optimization & Training — 6 citations into it
Whom Pierre Gentine trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- M. Khairoutdinov
- Diederik P. Kingma
- David A. Randall
- J. Andersen
- J. Sperry
- Sandrine Bony
- Geoffrey E. Hinton
- Jimmy Ba
- Christian Frankenberg
- Matthew Rodell
Who trusts Pierre Gentine overall
- Chaopeng Shen
- Jared D. Willard
- T. Bolton
- Stephan Rasp
- Peter Dueben
- Hugh Morrison
- Xiaowei Jia
- L. Zanna
Papers from Pierre Gentine's perspective
Ordered by how strongly the researchers Pierre Gentine trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Gradient-based learning applied to document recognition
- ImageNet Large Scale Visual Recognition Challenge
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Dropout: a simple way to prevent neural networks from overfitting
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6? (2020) — 128 citations
- Large influence of soil moisture on long-term terrestrial carbon uptake (2019) — 655 citations
- Coupling between the terrestrial carbon and water cycles—a review (2019) — 263 citations
- The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty (2019) — 1,357 citations
- Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems. (2019) — 364 citations
- A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks (2018) — 447 citations
- Deep learning to represent subgrid processes in climate models (2018) — 892 citations
- Could Machine Learning Break the Convection Parameterization Deadlock? (2018) — 417 citations
- Land–atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges (2018) — 850 citations