Jens Berg
Statistical and Nonlinear Physics · 2017–2018
A researcher in the Science corpus: 2 papers; trusts 40 researchers and is trusted by 33 researchers here, as inferred from the citation graph.
Subfields
- Statistical and Nonlinear Physics — 18 citations into it; #810 of 10,127 by trust here, #2,503 by citations
- Artificial Intelligence — 17 citations into it; #16,981 of 35,345 by trust here, #27,551 by citations
- AI for Physical Sciences — 10 citations into it
- Computational Mechanics — 8 citations into it; #1,329 of 10,001 by trust here, #3,362 by citations
- Numerical Methods & Scientific Computing — 8 citations into it
- Model Architectures — 7 citations into it
- Numerical Analysis — 6 citations into it
- Optimization & Training — 6 citations into it
Whom Jens Berg trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Maizar Raissi
- Roger Fletcher
- Kurt Hornik
- Hayden Schaeffer
- Robert Tibshirani
- Paris Perdikaris
- G. Em Karniadakis
- Isaac E. Lagaris
- George V. Cybenko
- S. Omohundro
Who trusts Jens Berg overall
- Han Gao
- Grant M. Rotskoff
- Hongwei Guo
- Luning Sun
- Ameya Dilip Jagtap
- Natarajan Sukumar
- G. Em Karniadakis
- Benjamin Moseley
- E. Kharazmi
- Martin Hutzenthaler
Papers from Jens Berg's perspective
Ordered by how strongly the researchers Jens Berg trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Going deeper with convolutions
Their papers
- Data-driven discovery of PDEs in complex datasets (2018) — 158 citations
- A unified deep artificial neural network approach to partial differential equations in complex geometries (2017) — 683 citations