2 citations · 3 across the 3 of their papers we have counts for
5 papers
A Simple and Efficient Stochastic Rounding Method for Training Neural Networks in Low Precision
Lu Xia, Martijn Anthonissen, Michiel Hochstenbach +1
Conventional stochastic rounding (CSR) is widely employed in the training of neural networks (NNs), showing promising training results even in low-precision computations. We introd…
Improved stochastic rounding
Lu Xia, Martijn Anthonissen, Michiel Hochstenbach +1
Due to the limited number of bits in floating-point or fixed-point arithmetic, rounding is a necessary step in many computations. Although rounding methods can be tailored for diff…
Multi-level neural networks for PDEs with uncertain parameters
Yous van Halder, Benjamin Sanderse, Barry Koren
A novel multi-level method for partial differential equations with uncertain parameters is proposed. The principle behind the method is that the error between grid levels in multi-…
Computing first passage times for Markov-modulated fluid models using numerical PDE problem solvers
Debarati Bhaumik, Marko A. A. Boon, Daan Crommelin +2
A popular method to compute first-passage probabilities in continuous-time Markov chains is by numerically inverting their Laplace transforms. Past decades, the scientific computin…
PDE/PDF-informed adaptive sampling for efficient non-intrusive surrogate modelling
Yous van Halder, Benjamin Sanderse, Barry Koren
A novel refinement measure for non-intrusive surrogate modelling of partial differential equations (PDEs) with uncertain parameters is proposed. Our approach uses an empirical inte…