Urban Fasel

Statistical and Nonlinear Physics · 2021

A researcher in the Science corpus: 1 paper; trusts 15 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 25 citations into it
• Artificial Intelligence — 13 citations into it
• Computational Mechanics — 11 citations into it
• Statistics and Probability — 10 citations into it
• AI for Physical Sciences — 6 citations into it
• Control and Systems Engineering — 5 citations into it

Whom Urban Fasel trusts overall

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

1. Steven L. Brunton
2. Igor Mezi'c
3. Leo Breiman
4. Bradley Efron
5. Hayden Schaeffer
6. David Q. Mayne
7. Andrew J. Majda
8. J. Nathan Kutz
9. Nicolai Meinshausen
10. Jean-Christophe Loiseau

Papers from Urban Fasel's perspective

Ordered by how strongly the researchers Urban Fasel 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. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Regression Shrinkage and Selection via the Lasso
5. Discovering governing equations from data by sparse identification of nonlinear dynamical systems
6. Bagging Predictors
7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
8. The Elements of Statistical Learning
9. Long Short-Term Memory
10. Reinforcement Learning: An Introduction

Their papers

• Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control (2021) — 371 citations