[Forschungsseminar-BSV] Forschungsseminar Computergrafik, Bildverarbeitung und Visualisierung
Vanessa Kretzschmar
kretzschmar at informatik.uni-leipzig.de
Do Jun 2 13:25:10 CEST 2022
Achtung:
- mehrere Vortraege (erhoehte Dauer moeglich)
E I N L A D U N G
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zum Forschungsseminar 'Computergrafik, Bildverarbeitung und Visualisierung'
am Mittwoch, den 8. Juni 2022, 13:15 Uhr,
im Raum P-701 im Paulinum am Augustusplatz, sowie über eine
Webkonferenz.
(https://conf.fmi.uni-leipzig.de/b/van-bwb-jil-vk2)
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Wir hören einen Vortrag von
Christofer Meinecke
mit dem Titel:
' Explaining Semi-Supervised Text Alignment through Visualization'
zum Inhalt:
The analysis of variance in complex text traditions is an arduous task
when carried out manually. Text alignment algorithms provide domain
experts with a robust alternative to such repetitive tasks. Existing
white-box approaches allow the digital humanities to establish
syntax-based
metrics taking into account the spelling, morphology and order of
words.
However, they produce limited results, as semantic meanings are
typically
not taken into account. Our interdisciplinary collaboration between
visualization and digital humanities combined a semi-supervised text
alignment approach based on word embeddings that take not only
syntactic
but also semantic text features into account, thereby improving the
overall quality of the alignment. In our collaboration, we developed
different visual interfaces that communicate the word distribution in
high-dimensional vector space generated by the underlying neural
network
for increased transparency, assessment of the tools reliability and
overall improved hypothesis generation. We further offer visual means
to enable the expert reader to feed domain knowledge into the system at
multiple levels with the aim of improving both the product and the
process
of text alignment. This ultimately illustrates how visualization can
engage with and augment complex modes of reading in the humanities.
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Weiterhin, hören wir einen Vortrag von
Leo Sperling (digital zugeschalten)
mit dem Titel:
'Uncertainty-aware Evaluation of Machine Learning Performance in
binary Classification Tasks'
zum Inhalt:
Machine learning has become a standard tool in computer vision.
Nowadays,
neural networks are one of the most prominent representatives in this
class of algorithms that usually require training and evaluation to
work
as desired. There exist a variety of evaluation metrics to
determine the
quality of a trained neural network, which are usually threshold
dependent.
This results in massive changes in the resulting evaluation when the
threshold is changed slightly. Further, measurements of uncertainty
such
as resulting from Bayesian approaches, are not considered in this
analysis.
In this paper, we present evaluation metrics for machine learning
approaches that are able to attach a probability distribution to the
utilized threshold and include uncertainty measures. We demonstrate the
applicability of our approach by applying the defined metrics to a
real-world example where a Bayesian neural network has been used to
predict stroke lesions
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Alle Interessierten sind im Namen von Professor Dr. Scheuermann herzlich
eingeladen.
Mit freundlichen Grüßen
Vanessa Kretzschmar
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