CS & Math PhD in Visual Learning Lab and Image & Pattern Analysis Group
Topic: Theory of Normalizing Flows
Supervisors: Ullrich Köthe and Christoph Schnörr
E-Mail: felix.draxler at iwr.uni-heidelberg.de
Twitter, GitHub, Google Scholar, ORCID
Funded by vector Stiftung (TRINN) and STRUCTURES.
Education
- M.Sc. Physics, Heidelberg University, Germany. 2018
- Thesis: „Energy Landscape of Neural Networks“
- Supervisor: Fred Hamprecht, Manfred Salmhofer
- Scholarship Max Weber-Programm by the State of Bavaria
- B.Sc. Physics, LMU Munich. 2015
- Thesis: „Evolutionary Optimization of a Cooling Sequence for the Generation of Ultracold Atoms“
- Supervision: Ulrich Schneider and Immanuel Bloch
- Semester abroad: winter 2015/16: INP Grenoble, France
- Scholarship Max Weber-Programm by the State of Bavaria
Publications
- Maximum Likelihood Training of Autoencoders (preprint, 2023)
Peter Sorrenson*, Felix Draxler*, Christoph Schnörr, Ullrich Köthe - Finding Competence Regions in Domain Generalization (TMLR 2023)
Jens Müller, Stefan T. Radev, Robert Schmier, Felix Draxler, Carsten Rother, Ullrich Köthe - On the Convergence Rate of Gaussianization with Random Rotations (ICML 2023)
Felix Draxler, Lars Kühmichel, Jens Müller, Armand Rousselot, Christoph Schnörr, Ullrich Köthe - Whitening Convergence Rate of Coupling-based Normalizing Flows (NeurIPS 2022) Oral, Scholar Award
Felix Draxler, Christoph Schnörr, Ullrich Köthe - Characterizing The Role of A Single Coupling Layer in Affine Normalizing Flows (GCPR 2020) Honorable Mention
Felix Draxler, Jonathan Schwarz, Christoph Schnörr, Ullrich Köthe - Riemannian SOS-Polynomial Normalizing Flows (GCPR 2020)
Jonathan Schwarz, Felix Draxler, Christoph Schnörr, Ullrich Köthe - On the Spectral Bias of Neural Networks (ICML 2019) Oral
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, Aaron Courville - Software library FrEIA (2018-2023)
Package to easily build invertible neureal networks with PyTorch
Lynton Ardizzone, Till Bungert, Felix Draxler, Ullrich Köthe, Jakob Kruse, Robert Schmier, Peter Sorrenson - Essentially No Barriers in Neural Network Energy Landscape (ICML 2018) Oral
Felix Draxler, Kambis Veschgini, Manfred Salmhofer, Fred Hamprecht
Invited Talks
Whitening Convergence Rate of Coupling-based Normalizing Flows
- Stefano Ermon lab, Stanford
- Marcus Brubaker lab, York University, Toronto
- Andrew Gordon Wilson lab, New York University
- NeurIPS 2022: Oral
Characterizing The Role of A Single Coupling Layer in Affine Normalizing Flows
- GCPR 2020: Oral
- Math Machine Learning seminar MPI MiS + UCLA via Guido Montúfar
Essentially No Barriers in Neural Network Energy Landscape
- ICML 2018: Long Oral
- Aspen Winter School 2019: Theoretical Physics for Machine Learning
- Smita Krishnaswamy lab, Yale