N. Mangan

Molecular Biology · 2016–2023

A researcher in the Science corpus: 3 papers; trusts 27 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 45 citations into it; #7,782 of 17,773 by trust here, #11,410 by citations
• Artificial Intelligence — 22 citations into it; #30,652 of 35,345 by trust here, #22,463 by citations
• Statistics and Probability — 20 citations into it
• Computational Mechanics — 17 citations into it
• Statistical and Nonlinear Physics — 15 citations into it; #8,550 of 10,127 by trust here, #5,200 by citations
• Computer Vision and Pattern Recognition — 9 citations into it
• Control and Systems Engineering — 8 citations into it; #7,099 of 8,257 by trust here, #3,865 by citations
• Genetics — 7 citations into it

Whom N. Mangan trusts overall

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

1. Steven L. Brunton
2. Robert Tibshirani
3. Michael D. Schmidt
4. Hod Lipson
5. Josh C. Bongard
6. Hirotogu Akaike
7. Hirotugu Akaike
8. J. Nathan Kutz
9. Richard Baraniuk
10. Joshua L. Proctor

Who trusts N. Mangan overall

1. Maizar Raissi
2. Steven L. Brunton
3. J. Nathan Kutz
4. Christopher Rackauckas
5. Daniel A. Messenger
6. Kadierdan Kaheman
7. Tong Qin
8. Eurika Kaiser
9. Yingbo Ma
10. David M. Bortz

Papers from N. Mangan's perspective

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

1. Regression Shrinkage and Selection via the Lasso
2. Addendum: Regularization and variable selection via the elastic net
3. Estimating the Dimension of a Model
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Nonlinear total variation based noise removal algorithms
7. ImageNet classification with deep convolutional neural networks
8. Compressed Sensing
9. Greed is good: algorithmic results for sparse approximation (IEEE Transactions on Information Theory)
10. Information Theory and an Extension of the Maximum Likelihood Principle

Their papers

• Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics (2016) — 429 citations
• Mega-scale experimental analysis of protein folding stability in biology and design (2023) — 306 citations
• Model selection for dynamical systems via sparse regression and information criteria (2017) — 331 citations