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20112023
most citedSKA Science Data Challenge 2: analysis and results

22 citations · 55 across the 10 of their papers we have counts for

collaborators

10 papers

stat.ML2023

MCMC-Correction of Score-Based Diffusion Models for Model Composition

Anders Sjöberg, Jakob Lindqvist, Magnus Önnheim +2

Diffusion models can be parameterized in terms of either score or energy function. The energy parameterization is attractive as it enables sampling procedures such as Markov Chain…

astro-ph.IM2023★ 22 cited

SKA Science Data Challenge 2: analysis and results

P. Hartley, A. Bonaldi, R. Braun +105

The Square Kilometre Array Observatory (SKAO) will explore the radio sky to new depths in order to conduct transformational science. SKAO data products made available to astronomer…

stat.CO2020★ 1 cited

NLMEModeling: A Wolfram Mathematica Package for Nonlinear Mixed Effects Modeling of Dynamical Systems

Jacob Leander, Joachim Almquist, Anna Johnning +2

Nonlinear mixed effects modeling is a powerful tool when analyzing data from several entities in an experiment. In this paper, we present NLMEModeling, a package for mixed effects…

cs.DS2019★ 2 cited

S-RASTER: Contraction Clustering for Evolving Data Streams

Gregor Ulm, Simon Smith, Adrian Nilsson +2

Contraction Clustering (RASTER) is a single-pass algorithm for density-based clustering of 2D data. It can process arbitrary amounts of data in linear time and in constant memory,…

cs.DC2019

Active-Code Replacement in the OODIDA Data Analytics Platform

Gregor Ulm, Emil Gustavsson, Mats Jirstrand

OODIDA (On-board/Off-board Distributed Data Analytics) is a platform for distributing and executing concurrent data analytics tasks. It targets fleets of reference vehicles in the…

cs.DS2019

Contraction Clustering (RASTER): A Very Fast Big Data Algorithm for Sequential and Parallel Density-Based Clustering in Linear Time, Constant Memory, and a Single Pass

Gregor Ulm, Simon Smith, Adrian Nilsson +2

Clustering is an essential data mining tool for analyzing and grouping similar objects. In big data applications, however, many clustering algorithms are infeasible due to their hi…