|Datum||Fr 11. Dezember 2020, 11:30 Uhr|
|Vorheriger Termin||Fr 27. November 2020|
|Nächster Termin||Do 17. Dezember 2020|
|Titel||Detecting Outlying Time-Series with Global Alignment Kernels|
|Kurzfassung||Using outlier detection algorithms, e.g., Support Vector Data Description (SVDD), for detecting outlying time-series usually requires extracting domain-specific attributes. However, this indirect way needs expert knowledge, making SVDD impractical for many real-world use cases. Incorporating "Global Alignment Kernels" directly into SVDD to compute the distance between time-series data bypasses the attribute-extraction step and makes the application of SVDD independent of the underlying domain.
In this work, we propose a new time-series outlier detection algorithm, combining "Global Alignment Kernels" and SVDD. Its outlier detection capabilities will be evaluated on synthetic data as well as on real-world data sets. Additionally, our approach's performance will be compared to state-of-the-art methods for outlier detection, especially with regard to the types of detected outliers.
|Titel||Meta-Modeling the Feature Space|
|Kurzfassung||Feature Selection is an important process in Machine Learning to improve model training times and complexity. One state-of-the art approach is Wrapper Feature Selection where subsets of features are evaluated. Because we can not evaluate all 2^n subsets an appropriate search strategy is vital.
Bayesian Optimization has already been successfully used in the context of hyperparameter optimization and very specific Feature Selection contexts. We want to look on how to use Bayesian Optimization for Feature Selection and discuss its limitations and possible solutions.
|Titel||Tuning of Explainable Artificial Intelligence (XAI) tools in the field of text analysis|
|Kurzfassung||Philipp Weinmann will present his plan for his Bachelor thesis with the title: Tuning of Explainable Artificial Intelligence (XAI) tools in the field of text analysis: He will present a global introduction to explainers for Artificial Intelligence in the context of NLP. We will then explore in details one of these tools: Shap, a perturbation based local explainer and talk about evaluating shap-explanations.|
- Neuen Vortrag erstellen