1 code implementation • 16 Feb 2023 • Joel Oskarsson, Per Sidén, Fredrik Lindsten
Our TGNN4I model is designed to handle both irregular time steps and partial observations of the graph.
no code implementations • ICLR 2023 • Hariprasath Govindarajan, Per Sidén, Jacob Roll, Fredrik Lindsten
With this interpretation, DINO assumes equal precision for all components when the prototypes are also $L^2$-normalized.
Ranked #24 on Self-Supervised Image Classification on ImageNet
Self-Supervised Image Classification Self-Supervised Learning +1
1 code implementation • 10 Jun 2022 • Joel Oskarsson, Per Sidén, Fredrik Lindsten
We propose a flexible GMRF model for general graphs built on the multi-layer structure of Deep GMRFs, originally proposed for lattice graphs only.
1 code implementation • ICML 2020 • Per Sidén, Fredrik Lindsten
Gaussian Markov random fields (GMRFs) are probabilistic graphical models widely used in spatial statistics and related fields to model dependencies over spatial structures.
no code implementations • 25 Jun 2019 • Per Sidén, Finn Lindgren, David Bolin, Anders Eklund, Mattias Villani
Bayesian whole-brain functional magnetic resonance imaging (fMRI) analysis with three-dimensional spatial smoothing priors have been shown to produce state-of-the-art activity maps without pre-smoothing the data.
Methodology Applications Computation