Zobrazeno 1 - 10
of 308
pro vyhledávání: '"Linsen Lars"'
Scatterplots provide a visual representation of bivariate data (or 2D embeddings of multivariate data) that allows for effective analyses of data dependencies, clusters, trends, and outliers. Unfortunately, classical scatterplots suffer from scalabil
Externí odkaz:
http://arxiv.org/abs/2408.06513
Sensitivity analyses of simulation ensembles determine how simulation parameters influence the simulation's outcome. Commonly, one global numerical sensitivity value is computed per simulation parameter. However, when considering 3D spatial simulatio
Externí odkaz:
http://arxiv.org/abs/2408.03817
Autor:
Evers, Marina, Linsen, Lars
Partitionings (or segmentations) divide a given domain into disjoint connected regions whose union forms again the entire domain. Multi-dimensional partitionings occur, for example, when analyzing parameter spaces of simulation models, where each seg
Externí odkaz:
http://arxiv.org/abs/2408.03641
When employing Direct Volume Rendering (DVR) for visualizing volumetric scalar fields, classification is generally performed on a piecewise constant or piecewise linear approximation of the field on a viewing ray. Smoothed Particle Hydrodynamics (SPH
Externí odkaz:
http://arxiv.org/abs/2401.02896
Voreen -- An Open-source Framework for Interactive Visualization and Processing of Large Volume Data
Technological advances for measuring or simulating volume data have led to large data sizes in many research areas such as biology, medicine, physics, and geoscience. Here, large data can refer to individual data sets with high spatial and/or tempora
Externí odkaz:
http://arxiv.org/abs/2207.12746
Publikováno v:
IEEE Visualization and Visual Analytics, 150-154 (2022)
Simulation ensembles are a common tool in physics for understanding how a model outcome depends on input parameters. We analyze an active particle system, where each particle can use energy from its surroundings to propel itself. A multi-dimensional
Externí odkaz:
http://arxiv.org/abs/2207.06519
Autor:
Evers, Marina, Linsen, Lars
Publikováno v:
Computers & Graphics 104 (2022), 140-151
Numerical simulations are commonly used to understand the parameter dependence of given spatio-temporal phenomena. Sampling a multi-dimensional parameter space and running the respective simulations leads to an ensemble of a large number of spatio-te
Externí odkaz:
http://arxiv.org/abs/2205.00980
Topic modeling is a state-of-the-art technique for analyzing text corpora. It uses a statistical model, most commonly Latent Dirichlet Allocation (LDA), to discover abstract topics that occur in the document collection. However, the LDA-based topic m
Externí odkaz:
http://arxiv.org/abs/2110.09247
Publikováno v:
Pattern Recognition. ICPR International Workshops and Challenges (2021) 130-145
Overfitting is one of the fundamental challenges when training convolutional neural networks and is usually identified by a diverging training and test loss. The underlying dynamics of how the flow of activations induce overfitting is however poorly
Externí odkaz:
http://arxiv.org/abs/2104.06153
Overfitting is one of the most common problems when training deep neural networks on comparatively small datasets. Here, we demonstrate that neural network activation sparsity is a reliable indicator for overfitting which we utilize to propose novel
Externí odkaz:
http://arxiv.org/abs/2002.09237