This website provides materials, exercises and further readings for the lectures "Probabilistic Graphical Models" and "Deep Learning".
Probabilistic Graphical Models
Jupyter Python notebooks for the vehicle localization example from the lecture:
Note: You can either look at the results of the Jupyter notebooks directly in your browser or execute & modify the Jupyter notebooks yourself. For executing the Jupyter notebook, download bp.ipynb, install Jupyter (see Jupyter documentation
) and execute the code. If you don't want to install Jupyter you can also visit https://try.jupyter.org/
, upload the notebook and run the Python interpreter directly in your browser.
- Modify the Python program bp.ipynb for a vehicle localization scenario with 4 lanes.
- Modify the Python program bp.ipynb for localizing two vehicles simultaneously. Introduce a new set of random variables representing the second vehicle and change the factor graph accordingly. Introduce additional pairwise factors which penalize the event of collision between the two vehicles.
- D. Rumelhart, G. Hinton and R. Williams: Learning representations by back-propagating errors. Nature, 1986.
- Y. LeCun, L. Bottou, Y. Bengio and Patrick Haffner: Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1989.
- M. Zeiler and R. Fergus: Visualizing and Understanding Convolutional Networks. ECCV, 2014.
- K. He, X. Zhang, S. Ren, and J. Sun: Deep Residual Learning for Image Recognition. CVPR, 2016.
- O. Vinyals, A. Toshev, S. Bengio and D. Erhan: Show and Tell: A Neural Image Caption Generator. CVPR, 2015.
Graphical Models & Deep Learning
- G. Hinton and R. Salakhutdinov: Reducing the Dimensionality of Data with Neural Networks. Science, 2006, Vol. 313. no. 5786, pp. 504--507.
- D. Kingma and M. Welling: Auto-encoding variational Bayes. ICLR, 2014.
- L. Chen, A. Schwing, A. Yuille and R. Urtasun: Learning Deep Structured Models. ICML, 2015.
- J. Domke: Learning graphical model parameters with approximate marginal inference. PAMI, 2013, Vol. 35, no. 10, pp. 2454--2467.