32 citations · 49 across the 8 of their papers we have counts for
5 papers · 1 filter
Information Hidden in Gradients of Regression with Target Noise
Arash Jamshidi, Katsiaryna Haitsiukevich, Kai Puolamäki
Second-order information -- such as curvature or data covariance -- is critical for optimisation, diagnostics, and robustness. However, in many modern settings, only the gradients…
GRADSTOP: Early Stopping of Gradient Descent via Posterior Sampling
Arash Jamshidi, Lauri Seppäläinen, Katsiaryna Haitsiukevich +3
Machine learning models are often learned by minimising a loss function on the training data using a gradient descent algorithm. These models often suffer from overfitting, leading…
Diffusion models as probabilistic neural operators for recovering unobserved states of dynamical systems
Katsiaryna Haitsiukevich, Onur Poyraz, Pekka Marttinen +1
This paper explores the efficacy of diffusion-based generative models as neural operators for partial differential equations (PDEs). Neural operators are neural networks that learn…
Improved Training of Physics-Informed Neural Networks with Model Ensembles
Katsiaryna Haitsiukevich, Alexander Ilin
Learning the solution of partial differential equations (PDEs) with a neural network is an attractive alternative to traditional solvers due to its elegance, greater flexibility an…
A Grid-Structured Model of Tubular Reactors
Katsiaryna Haitsiukevich, Samuli Bergman, Cesar de Araujo Filho +2
We propose a grid-like computational model of tubular reactors. The architecture is inspired by the computations performed by solvers of partial differential equations which descri…