Bethany Lusch

Statistical and Nonlinear Physics · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 33 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 77 citations into it; #1,633 of 10,127 by trust here, #2,158 by citations
• Computational Mechanics — 40 citations into it; #2,450 of 10,001 by trust here, #2,857 by citations
• Artificial Intelligence — 31 citations into it; #23,743 of 35,345 by trust here, #27,551 by citations
• Statistics and Probability — 25 citations into it
• Molecular Biology — 15 citations into it; #9,728 of 17,773 by trust here, #15,203 by citations
• Control and Systems Engineering — 10 citations into it
• AI for Physical Sciences — 9 citations into it
• Model Architectures — 9 citations into it

Whom Bethany Lusch trusts overall

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

1. Steven L. Brunton
2. Bernd R. Noack
3. J. Nathan Kutz
4. Igor Mezi'c
5. Clarence W. Rowley
6. Peter J. Schmid
7. Joshua L. Proctor
8. Diederik P. Kingma
9. Michael D. Schmidt
10. Sepp Hochreiter

Who trusts Bethany Lusch overall

1. Jared D. Willard
2. Hao Wu
3. Abhinav Narasingam
4. Eurika Kaiser
5. Steven L. Brunton
6. Naoya Takeishi
7. Zijie Li
8. Zhao Chen
9. Yinhao Zhu
10. Yohai Bar-Sinai

Papers from Bethany Lusch's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Gradient-based learning applied to document recognition
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Data-driven discovery of coordinates and governing equations (2019) — 1,027 citations
• Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders (2020) — 319 citations
• Deep learning for universal linear embeddings of nonlinear dynamics (2017) — 1,679 citations