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Department of
Computational Perception
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MACHINE LEARNING
Neural Networks
In recent years, neural networks outperformed traditional algorithms on many image classification tasks such as MNIST, CIFAR or ImageNet. Thus, advances in training methods or structural improvements of neural networks are evaluated mainly on image data. The direct applicability of new methods to other types of data (e.g. time-series data such as audio and music) often remains unclear. To investigate this, we offer the following types of master theses or practical projects:
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Contact: Rainer Kelz, Filip Korzeniowski, Gerhard Widmer |
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Conditional Random Fields
Conditional Random Fields are powerful models for structured prediction, where labels (or, classes) of consecutive data points are strongly correlated. While CRFs are often used in audio processing, they still rely on manually designed features. Recent advances in this field aim at leveraging the power of automatically learned features (using deep learning) for CRFs. In this context we offer the following topics for theses and practical works:
Contact: Filip Korzeniowski, Gerhard Widmer |
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