Charles Fefferman
Applied Mathematics · 1994–2019
A researcher in the Science corpus: 21 papers; trusts 27 researchers and is trusted by 43 researchers here, as inferred from the citation graph.
Subfields
- Applied Mathematics — 61 citations into it; #427 of 7,620 by trust here, #427 by citations
- Mathematical Physics — 33 citations into it; #3,568 of 8,562 by trust here, #2,478 by citations
- Geometry and Topology — 32 citations into it; #642 of 7,422 by trust here, #864 by citations
- Computational Mechanics — 31 citations into it
- Computational Theory and Mathematics — 26 citations into it; #10,944 of 12,182 by trust here, #9,804 by citations
- Computer Vision and Pattern Recognition — 12 citations into it
- Algebra and Number Theory — 9 citations into it
- Atomic and Molecular Physics, and Optics — 9 citations into it; #4,624 of 7,135 by trust here, #6,251 by citations
Whom Charles Fefferman trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Walter Briec
- Edward Bierstone
- Satyanad Kichenassamy
- Y. Brudnyi
- Matthew Brand
- A. Gover
- Pierre D. Milman
- William B. Johnson
- Richard Baraniuk
- Karol W. Hajduk
Who trusts Charles Fefferman overall
- R. Myers
- E. Gruyer
- Robin Graham
- Thomas Leistner
- Ling-Yan Hung
- Jeffrey S. Case
- Bo'az Klartag
- Andreas Čap
- P. Chesler
- Arie Israel
Papers from Charles Fefferman's perspective
Ordered by how strongly the researchers Charles Fefferman trusts endorse each paper. Same corpus, a different order for every researcher.
- Nonlinear dimensionality reduction by locally linear embedding.
- A global geometric framework for nonlinear dimensionality reduction.
- Gradient-based learning applied to document recognition
- Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- Extensions of Lipschitz mappings into Hilbert space
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Charting a Manifold (Neural Information Processing Systems)
- Estimation of Dependences Based on Empirical Data
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
Their papers
- Testing the Manifold Hypothesis (2013) — 743 citations
- Honeycomb Lattice Potentials and Dirac Points (2012) — 244 citations
- Whitney’s extension problems and interpolation of data (2008) — 114 citations
- A Generalized Sharp Whitney Theorem for Jets (2005) — 61 citations
- Simultaneous approximation in Lebesgue and Sobolev norms via eigenspaces (2019) — 56 citations
- Local Existence for the Non-Resistive MHD Equations in Nearly Optimal Sobolev Spaces (2016) — 148 citations
- Higher order commutator estimates and local existence for the non-resistive MHD equations and related models (2014) — 246 citations
- Wave Packets in Honeycomb Structures and Two-Dimensional Dirac Equations (2012) — 144 citations
- Sobolev extension by linear operators (2012) — 62 citations
- Juhl's formulae for GJMS operators and Q-curvatures (2012) — 82 citations
- Finite time singularities for the free boundary incompressible Euler equations (2011) — 139 citations
- Rayleigh-Taylor breakdown for the Muskat problem with applications to water waves (2011) — 175 citations
- Fitting a $C^m$-Smooth Function to Data II (2009) — 79 citations
- C m extension by linear operators (2007) — 101 citations
- The Ambient Metric (2007) — 542 citations
- Whitney’s extension problem for $C^m$ (2006) — 172 citations
- Interpolation and extrapolation of smooth functions by linear operators (2005) — 74 citations
- A sharp form of Whitney's extension theorem (2005) — 161 citations
- Ambient metric construction of Q-curvature in conformal and CR geometries (2003) — 159 citations
- $Q$-Curvature and Poincaré Metrics (2001) — 205 citations