Lars Ruthotto

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 5 papers; trusts 364 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 91 citations into it; #6,969 of 35,345 by trust here, #9,092 by citations
• Computer Vision and Pattern Recognition — 86 citations into it; #6,461 of 21,575 by trust here, #7,499 by citations
• Generative Models — 39 citations into it
• Model Architectures — 33 citations into it; #446 of 2,278 by trust here, #827 by citations
• Optimization & Training — 32 citations into it; #956 of 2,601 by trust here, #1,365 by citations
• Statistical and Nonlinear Physics — 28 citations into it; #693 of 10,127 by trust here, #954 by citations
• Probabilistic & Bayesian Methods — 24 citations into it
• Signal Processing — 17 citations into it

Whom Lars Ruthotto trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Yann LeCun
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Kaiming He
7. L. Eon Bottou
8. Ian J. Goodfellow
9. Sergey Ioffe
10. Max Welling

Who trusts Lars Ruthotto overall

1. Jens Behrmann
2. Xuechen Li
3. David Kristjanson Duvenaud
4. Leonardo Banh
5. A. Queiruga
6. Jacob Kelly
7. A. Sherstinsky
8. Yiping Lu
9. Jörn-Henrik Jacobsen
10. Ting-Kam Leonard Wong

Papers from Lars Ruthotto's perspective

Ordered by how strongly the researchers Lars Ruthotto trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Reversible Architectures for Arbitrarily Deep Residual Neural Networks (2017) — 298 citations
• Stable architectures for deep neural networks (2017) — 856 citations
• Deep Neural Networks Motivated by Partial Differential Equations (2018) — 570 citations
• An introduction to deep generative modeling (2021) — 305 citations
• OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport (2020) — 217 citations