Summer Semester 2025

Organizer: Karnik Ram (karnik.ram@tum.de)

Preliminary meeting : 11.02.25. 14:30 - 15:00 CET. Online – link will be posted shortly.

The current trend in deep learning is towards scaling model size and data, and more recently test-time compute. A smaller but steady trend has been in introducing certain physics-inspired inductive biases such as symmetries and dynamics into these models. Apart from being theoretically interesting, these methods also offer the promise of creating models that are data efficient and interpretable, which is important especially for many scientific problems. In this seminar we will look at important papers in this direction specifically focusing on equivariant and dynamical models.

• Students are expected to study one paper in depth, and present and lead a discussion on it. Apart from the presentation and report, students are also expected to periodically submit one-paragraph summaries of the papers discussed, and participate in the discussions.
• Sessions will be held in-person (with a remote attendance option) once every two weeks, on Tuesday afternoon (14:30 - 16:30). There will be two paper presentations in every session. There will also be a catch-up lecture on certain relevant topics from deep learning (eg. diffusion models, graph learning) at the start based on interest.
• All class-related communications are over Discord, and the summaries, presentation, and report are managed on Gradescope.

A good understanding of machine learning techniques (esp. deep learning), linear algebra, and calculus. Undergraduate students who are interested in enrolling should directly contact the organizer.

Location: TBD

Time: 2:30 PM to 4:30 PM