Maximilian Dax

According to our database, Maximilian Dax authored at least 15 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Reparameterized LLM Training via Orthogonal Equivalence Transformation.

Zeju Qiu, Simon Buchholz, Tim Z. Xiao, Maximilian Dax, Bernhard Schölkopf, Weiyang Liu

CoRR, June, 2025

Real-time inference for binary neutron star mergers using machine learning.

Maximilian Dax, Stephen R. Green, Jonathan Gair, Nihar Gupte, Michael Pürrer, Vivien Raymond, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf

Nat., March, 2025

Synthesizing 3D Abstractions by Inverting Procedural Buildings with Transformers.

Maximilian Dax, Jordi Berbel, Jan Stria, Leonidas J. Guibas, Urs Bergmann

CoRR, January, 2025

2024

Flow Matching for Atmospheric Retrieval of Exoplanets: Where Reliability meets Adaptive Noise Levels.

Timothy D. Gebhard, Jonas Wildberger, Maximilian Dax, Annalena Kofler, Daniel Angerhausen, Sascha P. Quanz, Bernhard Schölkopf

CoRR, 2024

Fast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimation.

Vladimir Starostin, Maximilian Dax, Alexander Gerlach, Alexander Hinderhofer, Álvaro Tejero-Cantero, Frank Schreiber

CoRR, 2024

Real-time gravitational-wave inference for binary neutron stars using machine learning.

Maximilian Dax, Stephen R. Green, Jonathan Gair, Nihar Gupte, Michael Pürrer, Vivien Raymond, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf

CoRR, 2024

2023

Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling.

Timothy D. Gebhard, Jonas Wildberger, Maximilian Dax, Daniel Angerhausen, Sascha P. Quanz, Bernhard Schölkopf

CoRR, 2023

Flow Matching for Scalable Simulation-Based Inference.

Jonas Wildberger, Maximilian Dax, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Adapting to noise distribution shifts in flow-based gravitational-wave inference.

Jonas Wildberger, Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf

CoRR, 2022

Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference.

Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf

CoRR, 2022

Group equivariant neural posterior estimation.

Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, Jakob H. Macke

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Real-time gravitational-wave science with neural posterior estimation.

Maximilian Dax, Stephen R. Green, Jonathan Gair, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf

CoRR, 2021

Explicitly Modeled Attention Maps for Image Classification.

Andong Tan, Duc Tam Nguyen, Maximilian Dax, Matthias Nießner, Thomas Brox

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2019

DeepUSPS: Deep Robust Unsupervised Saliency Prediction With Self-Supervision.

Duc Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi, Thi-Phuong-Nhung Ngo, Thi Hoai Phuong Nguyen, Zhongyu Lou, Thomas Brox

CoRR, 2019

DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision.

Duc Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi, Thi-Phuong-Nhung Ngo, Thi Hoai Phuong Nguyen, Zhongyu Lou, Thomas Brox

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019