Rima Alaifari: Catalogue data in Autumn Semester 2020 |
| Name | Prof. Dr. Rima Alaifari |
| Field | Applied Mathematics |
| Address | Seminar für Angewandte Mathematik ETH Zürich, HG G 59.2 Rämistrasse 101 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 32 00 |
| rima.alaifari@sam.math.ethz.ch | |
| URL | http://www.sam.math.ethz.ch/~rimaa |
| Department | Mathematics |
| Relationship | Assistant Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 401-4660-70L | Robustness of Deep Neural Networks  Number of participants limited to 40 | 4 credits | 2S | R. Alaifari | |
| Abstract | While deep neural networks have been very successfully employed in classification problems, their stability properties remain still unclear. In particular, the presence of so-called adversarial examples has demonstrated that state-of-the-art networks are extremely vulnerable to small perturbations in the data. | ||||
| Learning objective | In this seminar, we will consider the state-of-the-art in adversarial attacks and defenses. | ||||
| Prerequisites / Notice | Participants should already be familiar with the principles of deep neural networks. The course will also include programming that will require knowledge in using either PyTorch or Tensorflow. | ||||
| 401-5650-00L | Zurich Colloquium in Applied and Computational Mathematics  | 0 credits | 1K | R. Abgrall, R. Alaifari, H. Ammari, R. Hiptmair, S. Mishra, S. Sauter, C. Schwab | |
| Abstract | Research colloquium | ||||
| Learning objective | |||||