Zobrazeno 1 - 10
of 199
pro vyhledávání: '"Streit, Marc"'
Autor:
Stoiber, Christina, Moitzi, Daniela, Stitz, Holger, Grassinger, Florian, Prakash, Anto Silviya Geo, Girardi, Dominic, Streit, Marc, Aigner, Wolfgang
Visualization onboarding supports users in reading, interpreting, and extracting information from visual data representations. General-purpose onboarding tools and libraries are applicable for explaining a wide range of graphical user interfaces but
Externí odkaz:
http://arxiv.org/abs/2308.16559
ParaDime is a framework for parametric dimensionality reduction (DR). In parametric DR, neural networks are trained to embed high-dimensional data items in a low-dimensional space while minimizing an objective function. ParaDime builds on the idea th
Externí odkaz:
http://arxiv.org/abs/2210.04582
Autor:
Stoiber, Christina, Walchshofer, Conny, Pohl, Margit, Potzmann, Benjamin, Grassinger, Florian, Stitz, Holger, Streit, Marc, Aigner, Wolfgang
Publikováno v:
Elsevier Visual Informatics 2022
Comprehending and exploring large and complex data is becoming increasingly important for users in a wide range of application domains. Still, non-experts in visual data analysis often have problems with correctly reading and interpreting information
Externí odkaz:
http://arxiv.org/abs/2203.15418
Autor:
Stoiber, Christina, Wagner, Markus, Grassinger, Florian, Pohl, Margit, Stitz, Holger, Streit, Marc, Potzmann, Benjamin, Aigner, Wolfgang
Publikováno v:
Springer Nature 2022
The aim of visualization is to support people in dealing with large and complex information structures, to make these structures more comprehensible, facilitate exploration, and enable knowledge discovery. However, users often have problems reading a
Externí odkaz:
http://arxiv.org/abs/2203.11134
Autor:
Stoiber, Christina, Ceneda, Davide, Wagner, Markus, Schetinger, Victor, Gschwandtner, Theresia, Streit, Marc, Miksch, Silvia, Aigner, Wolfgang
A typical problem in Visual Analytics is that users are highly trained experts in their application domains, but have mostly no experience in using VA systems. Thus, users often have difficulties interpreting and working with visual representations.
Externí odkaz:
http://arxiv.org/abs/2202.02038
Autor:
Stoiber, Christina, Moitzi, Daniela, Stitz, Holger, Grassinger, Florian, Prakash, Anto Silviya Geo, Girardi, Dominic, Streit, Marc, Aigner, Wolfgang
Publikováno v:
In Visual Informatics September 2024 8(3):1-17
Classification is one of the most important supervised machine learning tasks. During the training of a classification model, the training instances are fed to the model multiple times (during multiple epochs) in order to iteratively increase the cla
Externí odkaz:
http://arxiv.org/abs/2007.11353
High-dimensional latent representations learned by neural network classifiers are notoriously hard to interpret. Especially in medical applications, model developers and domain experts desire a better understanding of how these latent representations
Externí odkaz:
http://arxiv.org/abs/2006.12902
Publikováno v:
ACM Trans. Interact. Intell. Syst. 11, 3-4, Article 22 (December 2021), 29 pages
In problem-solving, a path towards solutions can be viewed as a sequence of decisions. The decisions, made by humans or computers, describe a trajectory through a high-dimensional representation space of the problem. By means of dimensionality reduct
Externí odkaz:
http://arxiv.org/abs/2001.08372
Autor:
Hinterreiter, Andreas, Ruch, Peter, Stitz, Holger, Ennemoser, Martin, Bernard, Jürgen, Strobelt, Hendrik, Streit, Marc
Classifiers are among the most widely used supervised machine learning algorithms. Many classification models exist, and choosing the right one for a given task is difficult. During model selection and debugging, data scientists need to assess classi
Externí odkaz:
http://arxiv.org/abs/1910.00969