214 citations · 1.8k across the 88 of their papers we have counts for
94 papers
Stochastic Lanczos Quadrature for Computational Uncertainty in Linear Algebra
Hassan Fifen, Issa Karambal, Philipp Hennig
Applying a matrix function to a vector is a common operation in large-scale scientific computing, such as Bayesian inference. When the matrix is symmetric positive definite and the…
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions
Jonathan Schmidt, Philipp Hennig, Jörg Nick +1
Inference and simulation in the context of high-dimensional dynamical systems remain computationally challenging problems. Some form of dimensionality reduction is required to make…
Benchmarking Neural Network Training Algorithms
George E. Dahl, Frank Schneider, Zachary Nado +22
Training algorithms, broadly construed, are an essential part of every deep learning pipeline. Training algorithm improvements that speed up training across a wide variety of workl…
Uncertainty and Structure in Neural Ordinary Differential Equations
Katharina Ott, Michael Tiemann, Philipp Hennig
Neural ordinary differential equations (ODEs) are an emerging class of deep learning models for dynamical systems. They are particularly useful for learning an ODE vector field fro…
Probabilistic Exponential Integrators
Nathanael Bosch, Philipp Hennig, Filip Tronarp
Probabilistic solvers provide a flexible and efficient framework for simulation, uncertainty quantification, and inference in dynamical systems. However, like standard solvers, the…
Bayesian Numerical Integration with Neural Networks
Katharina Ott, Michael Tiemann, Philipp Hennig +1
Bayesian probabilistic numerical methods for numerical integration offer significant advantages over their non-Bayesian counterparts: they can encode prior information about the in…