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Technical University of Munich
Technical University of Munich

Seminar - Selected Topics in Machine Learning Research

Seminar - Selected Topics in Machine Learning Research (IN2107, IN4872)

Application

The pre-course meeting with information regarding the course format, possible topics etc. is scheduled for Feb 7, 2022 4pm on zoom (Passcode: 281710).

Note that you have to register via the matching system and fill out our application form to apply for a spot!

Schedule

  • Pre-course meeting: Feb 7, 2022 4pm - slides
  • Kick-off meeting: Apr 25, 2022 4pm
  • Final presentations: Jul 21 & 22, 2022

Prerequisites

This seminar is intended for Master's students only. You should have attended (and passed) the Machine Learning lecture (IN2064). Having attended Machine Learning for Graphs and Sequential Data (IN2323) or other advanced ML/DL lectures (IN2332, IN2346, etc.) is a plus.

Description

The amount of research in machine learning has grown exponentially in the last couple of years, uncovering many promising and successful research directions. In this seminar we will select and discuss a diverse set of topics of current research. This seminar will let students get acquainted with current machine learning research, let them explore new fields and ideas and let them analyze and criticize recent publications.

To do so, each student will receive 2-5 research papers which they should carefully read and analyze. Starting from these they should explore the surrounding literature and summarize their findings, criticism, and research ideas in a 4-page paper (double column). The students will then review each other's work to give valuable feedback and criticism. Finally, all students will prepare 25-minute presentations and present their work during a block seminar at the end of the semester.

Possible topics

  • Robustness
    • Robust Fine Tuning
    • Robustness Against Synthetic and Natural Perturbations
    • Attack Strategies
    • Certified Robustness of L-infinity Distance Nets
    • Computing Bounds on Network's Local Lipschitz Constant
    • Certifiably Robust Training using Lipschitz constants
    • A Data-Centric View On Robustness
  • Vision & NLP
    • Connecting Text and Images
    • Zero-shot Models \& Pre-training
  • Deep Learning Techniques
    • Equivariant Deep Learning
    • Knowledge Distillation
    • Quantization in Deep Learning
  • Physics & Simulation
    • Learning in Function Space with Neural Operators
    • Hamiltonian Neural Networks
  • Uncertainty Estimation
    • Uncertainty in Reinforcement Learning
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