lab [at] mis [dot] mpg [dot] de.| Week | Date | Time | Location | Speaker | Topic |
|---|---|---|---|---|---|
| Week 45 (2024) | Mon, 04.11.2024 | 15:00–16:30 | MIS, E2 10 | Diaaeldin Taha | Graph and Topological Neural Networks I |
| Week 46 (2024) | Mon, 11.11.2024 | 15:00–16:30 | MIS, E2 10 | Diaaeldin Taha | Graph and Topological Neural Networks II |
| Week 47 (2024) | Mon, 18.11.2024 | 15:00–16:30 | MIS, E2 10 | Parvaneh Joharinad | Group Equivariant Neural Networks I |
| Week 48 (2024) | Mon, 25.11.2024 | 15:00–16:30 | MIS, E2 10 | Parvaneh Joharinad | Group Equivariant Neural Networks II |
| Week 49 (2024) | Mon, 02.12.2024 | 15:00–16:30 | MIS, G3 10 | Nico Scherf | Deep Generative Models |
| Week 51 (2024) | Mon, 16.12.2024 | 15:00–16:30 | MIS, E2 10 | Jan Ewald | On the (Underestimated) Importance of Objective/Loss Functions |
| Week 3 (2025) | Mon, 13.01.2025 | 14:00–15:30 | MIS, E2 10 | Jan Ewald | Autoencoder and Their Variants for Biomedical Data |
| Week 4 (2025) | Mon, 20.01.2025 | 14:00–15:30 | MIS, E2 10 | Duc Luu | Learning Dynamical Systems I |
| Week 5 (2025) | Mon, 27.01.2025 | 14:00–15:30 | MIS, E2 10 | Duc Luu | Learning Dynamical Systems II |
| Week 6 (2025) | Mon, 03.02.2025 | 14:00–15:30 | MIS, E2 10 | Robert Haase | Large Language Models for Code Generation |
| Week 7 (2025) | Mon, 10.02.2025 | 14:00–15:30 | MIS, E2 10 | Guido Montufar | Foundations of Feature Learning I |
| Week 8 (2025) | Mon, 17.02.2025 | 14:00–15:30 | MIS, E2 10 | Guido Montufar | Foundations of Feature Learning II |
| Week 9 (2025) | Mon, 24.02.2025 | 14:00–15:30 | MIS, E2 10 | Paul Breiding | Computing with Varieties I |
| Week 10 (2025) | Mon, 03.03.2025 | 14:00–15:30 | MIS, E2 10 | Paul Breiding | Computing with Varieties II |
| Week 11 (2025) | Mon, 10.03.2025 | 14:00–15:30 | MIS, E2 10 | Angelica Torres | Varieties in Machine Learning I |
| Week 12 (2025) | Mon, 17.03.2025 | 14:00–15:30 | MIS, E2 10 | Angelica Torres | Varieties in Machine Learning II |
| Week 13 (2025) | Mon, 24.03.2025 | 14:00–15:30 | MIS, E2 10 | Marzieh Eidi | Geometric Machine Learning |
Speaker: Diaaeldin Taha (Max Planck Institute for Mathematics in the Science, Germany)
Description: In these two sessions, we will provide an overview of deep learning with a focus on graph and topological neural networks. We will begin by reviewing neural networks, parameter estimation, and the universal approximation theorem. Then, we will discuss graphs and motivate graph convolutional neural networks by tracing their origins from spectral filters in signal processing. Lastly, we will review recent progress in topological deep learning, particularly focusing on simplicial, cellular, and hypergraph neural networks as extensions of graph neural networks. We will assume a basic familiarity with linear algebra and calculus; all relevant concepts from graph theory and topology will be introduced.
References:
Speaker: Parvaneh Joharinad
Speaker: Nico Scherf
Speaker: Jan Ewald
Speaker: Duc Luu
Speaker: Robert Hasse
Speaker: Guido Montufar
Speaker: Paul Breiding
Speaker: Angelica Torres
Speaker: Marzieh Eidi