Jakob Zech

Statistics and Probability · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Jakob Zech trusts overall

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

  1. Christoph Schwab
  2. Albert Cohen
  3. Abdellah Chkifa
  4. Thomas Gerstner

Papers from Jakob Zech's perspective

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

  1. Approximation theory of the MLP model in neural networks
  2. Why Deep Neural Networks for Function Approximation? (International Conference on Learning Representations)
  3. Breaking the curse of dimensionality in sparse polynomial approximation of parametric PDEs
  4. Convergence rates for sparse chaos approximations of elliptic problems with stochastic coefficients
  5. Galerkin Finite Element Approximations of Stochastic Elliptic Partial Differential Equations (SIAM Journal on Numerical Analysis)
  6. Data Oscillation and Convergence of Adaptive FEM (SIAM Journal on Numerical Analysis)
  7. Optimality of a Standard Adaptive Finite Element Method (Foundations of Computational Mathematics)
  8. Interpolation and Approximation
  9. Adaptive wavelet methods for elliptic operator equations: Convergence rates (Mathematics of Computation)
  10. Adaptive Finite Element Methods with convergence rates (Numerische Mathematik)

Their papers