Student Projects in Machine Learning and Artificial Intelligence
Much of our research applies AI and Machine Learning (see further below), but we are also interested in developing, improving or understanding Deep Learning or Statistical Learning/Modeling in general: How does it work? When does it fail? What can we do when data is scarce? Projects may involve re-implementing methods from the literature, systematic experiments with designed data sets, attempts at qualitative or performance improvements, etc. These projects generally require a background in Machine Learning.
These student projects can be started any time (including holidays) and can span over semester boundaries.
Remark: We are open for new proposals - if you are interested in Intelligent Music Processing, feel free to contact us!
Interpretable Machine Learning:
- Evaluation measures for interpretability methods
- On the Robustness of Interpretability Methods
- Local Interpretable Model-Agnostic Explanations
- What do Deep Networks Like to See?
- Gradient-based Attribution Methods (https://arxiv.org/abs/1711.06104, ...)
- Frameworks for IML (https://github.com/oracle/Skater, https://eli5.readthedocs.io/en/latest/)
- Interpretable Machine Learning for RNNs/LSTMs
- Deep InfoMax
- Invariant information clustering
- Disentangled representation learning (https://arxiv.org/abs/1811.12359, https://arxiv.org/abs/1701.00160)
- Generating raw audio GANs / WaveNet (https://openai.com/blog/generative-models/, https://arxiv.org/abs/1609.03499)
- DDSP: Differentiable Digital Signal Processing (https://magenta.tensorflow.org/ddsp)
- Flow-based models (http://akosiorek.github.io/ml/2018/04/03/norm_flows.html)
Machine Learning Theory: