PHILIPP ENGLER(AG ARTIFICIAL INTELLIGENCE, PROF. DENGEL)
hosted by Ph.D. Program in CS @ TU KL
"APPROACHES TO TIME SERIES ANALYSIS USING DATA AUGMENTATION TECHNIQUES AND UNLABELED DATA"
Time Series data arises from sensors everywhere around us and in various industries. They can contain valuable information that may require manual inspection by an expert to be uncovered or contain structures that remain unrecognized by humans. Machine learning algorithms can help us uncover hidden information in time series data, which may be used for more precise medical diagnoses, increased efficiency of machines, or various quality-of-life improvements in smart devices. For my Ph.D. thesis, I aim to develop time series generation techniques to augment available data, as well as unsupervised learning techniques to leverage large amounts of unlabeled data in order to overcome the bottleneck of hand-annotating datasets, which often is one of the major obstacles for real-world application of deep neural networks.
|Time:||Thursday, 15.06.2023, 09:00|
|Place:||Blechhammer Hotel-Restaurant in Kaiserslautern|