Description
In this seminar, recent papers of the machine learning literature are presented and discussed. Students will learn how to structure a scientific presentation in English which covers the key ideas of a scientific paper. An important goal of the seminar presentation is to summarize the essential ideas of the paper in sufficient depth while omitting details which are not essential for the understanding of the work. The presentation style will play an important role and should reach the level of professional scientific presentations.
Class Info
Schedules
Slides (Thursday slot)
| Date | Student | Topic |
|---|---|---|
| Oct 19 | Ernst Oldenhof | Weight Uncertainty in Neural Networks |
| Erik Daxberger | Dropout as a Bayesian Approximation | |
| Oct 26 | Robin Spiess | Uncertainties for Bayesian Deep Learning for Computer Vision |
| Rémi Pautrat | Auto-Encoding Variational Bayes | |
| Nov 02 | Amirezza Bahreini | Train Faster, Generalize Better |
| Jean Garret | Matrix Completion has no Spurious Local Minimum | |
| Nov 09 | Antonio Orvieto | Gradient Descent Learns Linear Dynamical Systems |
| Mohammad Reza Karimi | Failures of Gradient-Based Deep Learning | |
| Nov 16 | Adyasha Dash | Inherent Trade-Offs in the Fair Determination of Risk Score |
| Andreas Psimopoulos | Deep Learning for Predicting Human Strategic Behaviour | |
| Nov 23 | Moritz Zilian | Equality of Opportunity in Supervised Learning |
| Nov 30 | Marko Trauber | Why Should I Trust You? Explaining the Predictions of any Classifier |
| Dec 14 | Timo Bräm | Action-Conditional Video Prediction using Deep Networks |
| Yu-chen Tsai | Value Iteration Networks |
Use your nethz credentials to access the slides.
Contact
Professors:
Prof. Joachim Buhmann,
Prof. Thomas Hofmann,
Prof. Andreas Krause,
Prof. Gunnar Rätsch
Assistants:
Dr. Rebekka Burkholz,
Dr. Luis Haug,
Dr. Jens Witkowski