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Zech, Jakob

  1. Mathematical theory of deep learning
    2024/07/25 by Philipp Petersen, Petersen, Philipp, Jakob Zech +1 · 5 voices · 6 citations
    Computer Science · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #History and Overview (math.HO) #Machine Learning (cs.LG) #Neural Networks and Applications #cs.LG #math.HO
  2. Deep Operator Network Approximation Rates for Lipschitz Operators
    2023/07/19 by Christoph Schwab, Andreas Stein, Schwab, Christoph +3 · 4 citations
    Mathematics · Computer Science · Physics and Astronomy · #Numerical methods in inverse problems #Advanced Mathematical Modeling in Engineering #Model Reduction and Neural Networks
  3. Analyticity and sparsity in uncertainty quantification for PDEs with Gaussian random field inputs
    2022/01/06 by Ðinh Dũng, Van Kien Nguyen, Dũng, Dinh +5 · 3 citations
    Decision Sciences · #FOS: Mathematics #Numerical Analysis (math.NA) #Probabilistic and Robust Engineering Design
  4. Neural and spectral operator surrogates: unified construction and expression rate bounds
    2022/07/11 by Lukas Herrmann, Herrmann, Lukas, Christoph Schwab +3 · 3 citations
    Computer Science · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Neural Networks and Applications #Numerical Analysis (math.NA)
  5. Sparse approximation of triangular transports. Part I: the finite dimensional case
    2020/06/12 by Zech, Jakob, Marzouk, Youssef · 2 citations
    #32D05 #41A10 #41A25 #41A46 #62D99 #65D15 #FOS: Mathematics #Numerical Analysis (math.NA) #Statistics Theory (math.ST)
  6. Deep Learning in High Dimension: Neural Network Approximation of Analytic Functions in L2(ℝd,γd)
    2021/11/13 by Christoph Schwab, Schwab, Christoph, Jakob Zech +1 · 2 citations
    Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Mathematical Approximation and Integration #Numerical Analysis (math.NA) #Probability (math.PR)
  7. Metropolis-adjusted interacting particle sampling
    2023/12/21 by Björn Sprungk, Sprungk, Björn, Simon Weißmann +3 · 4 citations
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Markov Chains and Monte Carlo Methods #Numerical Analysis (math.NA) #Statistical Methods and Bayesian Inference
  8. Optimal Scheduling of Dynamic Transport
    2025/04/19 by Tsimpos, Panos, Ren, Zhi, Zech, Jakob +1 · 1 voice · 5 citations
    #Classical Analysis and ODEs (math.CA) #FOS: Computer and information sciences #FOS: Mathematics #Functional Analysis (math.FA) #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  9. Distribution learning via neural differential equations: a nonparametric statistical perspective
    2023/09/03 by Marzouk, Youssef, Ren, Zhi, Wang, Sven +1 · 2 citations
    #Classical Analysis and ODEs (math.CA) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistics Theory (math.ST)
  10. Multilevel Optimization for Inverse Problems
    2022/04/28 by Simon Weißmann, Ashia Wilson, Weissmann, Simon +3 · 2 citations
    Computer Science · #65K10 #65N21 #65N75 #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Neural Networks and Applications #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Stochastic Gradient Optimization Techniques
  11. On the mean field limit of consensus based methods
    2024/09/05 by Koß, Marvin, Weissmann, Simon, Zech, Jakob · 3 citations
    #FOS: Mathematics #Optimization and Control (math.OC) #Probability (math.PR)
  12. Statistical Learning Theory for Neural Operators
    2024/12/23 by Niklas Reinhardt, Sven Wang, Reinhardt, Niklas +3 · 4 citations
    Computer Science · #Neural Networks and Applications
  13. On the mean-field limit for Stein variational gradient descent: stability and multilevel approximation
    2024/02/02 by Weissmann, Simon, Zech, Jakob · 1 citation
    #FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Numerical Analysis (math.NA)
  14. Distribution learning via neural differential equations: minimal energy regularization and approximation theory
    2025/02/06 by Youssef Marzouk, Marzouk, Youssef, Zhi Ren +3 · 2 citations
    Physics and Astronomy · #Model Reduction and Neural Networks
  15. Low Stein Discrepancy via Message-Passing Monte Carlo
    2025/03/27 by Kirk, Nathan, Rusch, T. Konstantin, Zech, Jakob +1 · 3 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Numerical Analysis (math.NA)