3 papers
cs.CV2026
Kolmogorov-Arnold Networks for Spatially Independent Multispectral Land Classification
Katherine L. Bauer, Teemu Harkonen, Simo Sarkka +1
Land classification from satellite imagery is important for land management, environmental monitoring, and urban planning. Machine learning methods such as random forests and multi…
stat.AP2026
Bayesian estimation of optical constants using mixtures of Gaussian process experts
Teemu Härkönen, Hui Chen, Erik Vartiainen
We propose modeling absorption spectrum measurements as mixtures of Gaussian process experts. This enables us to construct a flexible statistical model for interpolating and extrap…
stat.ME2026
Estimation of log-Gaussian gamma processes with iterated posterior linearization and Hamiltonian Monte Carlo
Teemu Härkönen, Simo Särkkä
Stochastic processes are a flexible and widely used family of models for statistical modeling. While stochastic processes offer attractive properties such as inclusion of uncertain…