Sebastian Reich
Atmospheric Science · 2000–2020
A researcher in the Science corpus: 5 papers; trusts 22 researchers and is trusted by 23 researchers here, as inferred from the citation graph.
Subfields
- Atmospheric Science — 17 citations into it; #1,184 of 4,804 by trust here, #2,716 by citations
- Global and Planetary Change — 16 citations into it
- Artificial Intelligence — 14 citations into it
- Statistics and Probability — 14 citations into it; #9,568 of 12,932 by trust here, #8,740 by citations
- Statistical and Nonlinear Physics — 13 citations into it; #3,194 of 10,127 by trust here, #5,625 by citations
- Computational Mechanics — 10 citations into it; #2,678 of 10,001 by trust here, #6,877 by citations
- Numerical Analysis — 10 citations into it; #583 of 3,586 by trust here, #959 by citations
- Probabilistic & Bayesian Methods — 10 citations into it; #1,236 of 1,525 by trust here, #1,274 by citations
Whom Sebastian Reich trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Qiang Liu
- Ernst Hairer
- Felix Otto
- Jerrold E. Marsden
- Cédric Villani
- Aapo Hyvärinen
- Ralf Hiptmair
- Thomas J. Bridges
- Jeffrey L. Anderson
- Bernhard Schölkopf
Who trusts Sebastian Reich overall
- Thomas J. Bridges
- Uri M. Ascher
- Jerrold E. Marsden
- Robert I. McLachlan
- Matthew West
- Kaushik Bhattacharya
- Mark Kärcher
- Yang Song
- Enrico Camporeale
- Sébastien Boyaval
Papers from Sebastian Reich's perspective
Ordered by how strongly the researchers Sebastian Reich 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)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
Their papers
- Interacting Particle Solutions of Fokker–Planck Equations Through Gradient–Log–Density Estimation (2020) — 139 citations
- Multi-Symplectic Runge—Kutta Collocation Methods for Hamiltonian Wave Equations (2000) — 301 citations
- Multi-symplectic integrators: numerical schemes for Hamiltonian PDEs that conserve symplecticity (2001) — 483 citations
- Probabilistic Forecasting and Bayesian Data Assimilation (2015) — 428 citations
- Numerical methods for Hamiltonian PDEs (2006) — 239 citations