Maximilian Dax

19 papers A* 4Journal 15
YearRankTypeTitle / Venue / Authors
2025 J jnl
Flexible Gravitational-Wave Parameter Estimation with Transformers.
CoRR
Annalena Kofler, Maximilian Dax, Stephen R. Green, Jonas Wildberger, Nihar Gupte, Jakob H. Macke, Jonathan Gair, Alessandra Buonanno, Bernhard Schölkopf
2025 J jnl
Real-time inference for binary neutron star mergers using machine learning.
Nat.
Maximilian Dax, Stephen R. Green, Jonathan Gair, Nihar Gupte, Michael Pürrer, Vivien Raymond, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf
2025 J jnl
Reparameterized LLM Training via Orthogonal Equivalence Transformation.
CoRR
Zeju Qiu, Simon Buchholz, Tim Z. Xiao, Maximilian Dax, Bernhard Schölkopf, Weiyang Liu
2025 J jnl
Synthesizing 3D Abstractions by Inverting Procedural Buildings with Transformers.
CoRR
Maximilian Dax, Jordi Berbel, Jan Stria, Leonidas J. Guibas, Urs Bergmann
2024 J jnl
Fast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimation.
CoRR
Vladimir Starostin, Maximilian Dax, Alexander Gerlach, Alexander Hinderhofer, Álvaro Tejero-Cantero, Frank Schreiber
2024 J jnl
Flow Matching for Atmospheric Retrieval of Exoplanets: Where Reliability meets Adaptive Noise Levels.
CoRR
Timothy D. Gebhard, Jonas Wildberger, Maximilian Dax, Annalena Kofler, Daniel Angerhausen, Sascha P. Quanz, Bernhard Schölkopf
2024 J jnl
Real-time gravitational-wave inference for binary neutron stars using machine learning.
CoRR
Maximilian Dax, Stephen R. Green, Jonathan Gair, Nihar Gupte, Michael Pürrer, Vivien Raymond, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf
2023 A* conf
Flow Matching for Scalable Simulation-Based Inference.
NeurIPS
Jonas Wildberger, Maximilian Dax, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf
2023 J jnl
Flow Matching for Scalable Simulation-Based Inference.
CoRR
Maximilian Dax, Jonas Wildberger, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf
2023 J jnl
Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling.
CoRR
Timothy D. Gebhard, Jonas Wildberger, Maximilian Dax, Daniel Angerhausen, Sascha P. Quanz, Bernhard Schölkopf
2022 J jnl
Adapting to noise distribution shifts in flow-based gravitational-wave inference.
CoRR
Jonas Wildberger, Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf
2022 A* conf
Group equivariant neural posterior estimation.
ICLR
Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, Jakob H. Macke
2022 J jnl
Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference.
CoRR
Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf
2021 A* conf
Explicitly Modeled Attention Maps for Image Classification.
AAAI
Andong Tan, Duc Tam Nguyen, Maximilian Dax, Matthias Nießner, Thomas Brox
2021 J jnl
Group equivariant neural posterior estimation.
CoRR
Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, Jakob H. Macke
2021 J jnl
Real-time gravitational-wave science with neural posterior estimation.
CoRR
Maximilian Dax, Stephen R. Green, Jonathan Gair, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf
2020 J jnl
Explicitly Modeled Attention Maps for Image Classification.
CoRR
Andong Tan, Duc Tam Nguyen, Maximilian Dax, Matthias Nießner, Thomas Brox
2019 J jnl
DeepUSPS: Deep Robust Unsupervised Saliency Prediction With Self-Supervision.
CoRR
Duc Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi, Thi-Phuong-Nhung Ngo, Thi Hoai Phuong Nguyen, Zhongyu Lou, Thomas Brox
2019 A* conf
DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision.
NeurIPS
Duc Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi, Thi-Phuong-Nhung Ngo, Thi Hoai Phuong Nguyen, Zhongyu Lou, Thomas Brox