Christof Schutte

Statistical and Nonlinear Physics · 1999–2019

A researcher in the Science corpus: 10 papers; trusts 45 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 51 citations into it; #2,476 of 10,127 by trust here, #2,849 by citations
• Molecular Biology — 42 citations into it; #2,887 of 17,773 by trust here, #5,819 by citations
• Statistics and Probability — 26 citations into it; #9,998 of 12,932 by trust here, #5,294 by citations
• Artificial Intelligence — 15 citations into it
• Computational Mechanics — 14 citations into it
• Atomic and Molecular Physics, and Optics — 13 citations into it; #1,413 of 7,135 by trust here, #2,531 by citations
• Computational Theory and Mathematics — 12 citations into it
• Materials Chemistry — 12 citations into it

Whom Christof Schutte trusts overall

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

1. Nicholas I. Fisher
2. Stéphane Lafon
3. Jianbo Shi
4. Peter J. Schmid
5. Sean P. Meyn
6. Ronald R. Coifman
7. Feliks Nuske
8. Frank No'e
9. Alexander Fischer
10. Sam T. Roweis

Who trusts Christof Schutte overall

1. Gary Froyland
2. Frank No'e
3. Hao Wu
4. Wilhelm Huisinga
5. Feliks Nuske
6. Peter Deuflhard
7. Andreas Mardt
8. Marco Sarich
9. Stefan Klus
10. Gregory R. Bowman

Papers from Christof Schutte's perspective

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

1. Normalized cuts and image segmentation
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet classification with deep convolutional neural networks
7. Diffusion maps
8. Dynamic mode decomposition of numerical and experimental data
9. Nonlinear dimensionality reduction by locally linear embedding.
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Markov models of molecular kinetics: generation and validation. (2011) — 1,235 citations
• On the Approximation Quality of Markov State Models (2010) — 178 citations
• Transition Path Theory for Markov Jump Processes (2009) — 461 citations
• Data-driven approximation of the Koopman generator: Model reduction, system identification, and control (2019) — 310 citations
• Transfer Operator Approach to Conformational Dynamics in Biomolecular Systems (2001) — 132 citations
• Data-Driven Model Reduction and Transfer Operator Approximation (2017) — 323 citations
• Metastability and Markov State Models in Molecular Dynamics Modeling, Analysis (2016)
• On the numerical approximation of the Perron-Frobenius and Koopman operator (2015) — 332 citations
• Quantum-classical Liouville approach to molecular dynamics: Surface hopping Gaussian phase-space packets (2002) — 141 citations
• A Direct Approach to Conformational Dynamics Based on Hybrid Monte Carlo (1999)