7 citations · 8 across the 7 of their papers we have counts for
4 papers · 1 filter
Adaptive Probabilistic ODE Solvers Without Adaptive Memory Requirements
Nicholas Krämer
Despite substantial progress in recent years, probabilistic solvers with adaptive step sizes can still not solve memory-demanding differential equations -- unless we care only abou…
Numerically Robust Fixed-Point Smoothing Without State Augmentation
Nicholas Krämer
Practical implementations of Gaussian smoothing algorithms have received a great deal of attention in the last 60 years. However, almost all work focuses on estimating complete tim…
A tutorial on automatic differentiation with complex numbers
Nicholas Krämer
Automatic differentiation is everywhere, but there exists only minimal documentation of how it works in complex arithmetic beyond stating "derivatives in " "d…
Gradients of Functions of Large Matrices
Nicholas Krämer, Pablo Moreno-Muñoz, Hrittik Roy +1
Tuning scientific and probabilistic machine learning models for example, partial differential equations, Gaussian processes, or Bayesian neural networks often relies on eva…