Semantische Suche
Freitag, 29. Oktober 2021, 11:30 Uhr
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| Vortragende(r) | Klevia Ulqinaku |
|---|---|
| Titel | Analysis and Visualization of Semantics from Massive Document Directories |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Edouard Fouché |
| Vortragsmodus | |
| Kurzfassung | Research papers are commonly classified into categories, and we can see the existing contributions as a massive document directory, with sub-folders. However, research typically evolves at an extremely fast pace; consider for instance the field of computer science. It can be difficult to categorize individual research papers, or to understand how research communities relate to each other.
In this thesis we will analyze and visualize semantics from massive document directories. The results will be displayed using the arXiv corpus, which contains domain-specific (computer science) papers of the past thirty years. The analysis will illustrate and give insight about past trends of document directories and how their relationships evolve over time. |
Freitag, 29. Oktober 2021, 14:00 Uhr
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Ort: Raum 348 (Gebäude 50.34)
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(Keine Vorträge)
Freitag, 5. November 2021, 11:30 Uhr
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Ort: Raum 348 (Gebäude 50.34)
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| Vortragende(r) | Tobias Haßberg |
|---|---|
| Titel | Development of an Active Learning Approach for One Class Classification using Bayesian Uncertainty |
| Vortragstyp | Proposal |
| Betreuer(in) | Bela Böhnke |
| Vortragsmodus | |
| Kurzfassung | HYBRID: This Proposal will be online AND in the seminar room 348.
When working with large data sets, in many situations one has to deals with a large set data from a single class and only few negative examples from other classes. Learning classifiers, which can assign data points to one of the groups, is known as one-class classification (OCC) or outlier detection. The objective of this thesis is to develop and evaluate an active learning process to train an OCC. The process uses domain knowledge to reasonably adopt a prior distribution. Knowing that prior distribution, query strategies will be evaluated, which consider the certainty, more detailed the uncertainty, of the estimated class membership scorings. The integration of the prior distribution and the estimation of uncertainty, will be modeled using a gaussian process. |
Freitag, 5. November 2021, 12:00 Uhr
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| Vortragende(r) | Frederik Scheiderbauer |
|---|---|
| Titel | Automatisiertes Black-Box Software Testing mit neuartigen neuronalen Netzen |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Daniel Zimmermann |
| Vortragsmodus | |
| Kurzfassung | Das Testen von Softwareprojekten ist mit einem hohen Arbeitsaufwand verbunden, dies betrifft insbesondere die grafische Benutzeroberfläche.
Verfahren der künstlichen Intelligenz auf der Grundlage neuronaler Netzwerke können genutzt werden, um viele der besonders aufwändigen Aufgaben schneller oder sogar besser zu lösen als herkömmliche Methoden. In dieser Arbeit wird ein neuartiges neuronales Netzwerk auf seine Fähigkeit hin untersucht, eine Software allein anhand der Pixeldaten ihrer Benutzeroberfläche zu testen. Des Weiteren wird ein Framework entwickelt, welches mithilfe von leistungsfähigen GPUs den Trainingsvorgang deutlich beschleunigen kann. |
Freitag, 12. November 2021, 11:30 Uhr
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| Vortragende(r) | Li Mingyi |
|---|---|
| Titel | On the Converge of Monte Carlo Dependency Estimators |
| Vortragstyp | Proposal |
| Betreuer(in) | Edouard Fouché |
| Vortragsmodus | |
| Kurzfassung | Estimating dependency is essential for data analysis. For example in biological analysis, knowing the correlation between groups of proteins and genes may help predict genes functions, which makes cure discovery easier.
The recently introduced Monte Carlo Dependency Estimation (MCDE) framework defines the dependency between a set of variables as the expected value of a stochastic process performed on them. In practice, this expected value is approximated with an estimator which iteratively performs a set of Monte Carlo simulations. In this thesis, we propose several alternative estimators to approximate this expected value. They function in a more dynamic way and also leverage information from previous approximation iterations. Using both probability theory and experiments, we show that our new estimators converge much faster than the original one. |