Samuel E. Otto

Statistical and Nonlinear Physics · 2017

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 17 citations into it; #1,728 of 10,127 by trust here, #5,403 by citations
• Computational Mechanics — 11 citations into it; #2,055 of 10,001 by trust here, #5,567 by citations
• Artificial Intelligence — 8 citations into it
• Computer Vision and Pattern Recognition — 5 citations into it

Whom Samuel E. Otto trusts overall

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

1. Djork-Arné Clevert
2. Clarence W. Rowley
3. Thomas Unterthiner
4. Peter J. Schmid
5. Sepp Hochreiter
6. Igor Mezi'c
7. Bernd R. Noack

Who trusts Samuel E. Otto overall

1. Jared D. Willard
2. Hao Wu
3. Naoya Takeishi
4. Enoch Yeung
5. Kathleen P. Champion
6. Xiaowei Jia
7. Frank No'e
8. Bethany Lusch
9. Shaoming Xu
10. Steven L. Brunton

Papers from Samuel E. Otto's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Gradient-based learning applied to document recognition
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Dropout: a simple way to prevent neural networks from overfitting

Their papers

• Linearly-Recurrent Autoencoder Networks for Learning Dynamics (2017) — 409 citations