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 (NeurIPS, 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)

Explicitly Modeled Attention Maps for Image Classification. - Andong Tan; Duc Tam Nguyen; Maximilian Dax; Matthias Nießner; Thomas Brox (AAAI, 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)

Group equivariant neural posterior estimation. - Maximilian Dax; Stephen R. Green; Jonathan Gair; Michael Deistler; Bernhard Schölkopf; Jakob H. Macke (ICLR, 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)

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)

Flow Matching for Scalable Simulation-Based Inference. - Jonas Wildberger; Maximilian Dax; Simon Buchholz; Stephen R. Green; Jakob H. Macke; Bernhard Schölkopf (NeurIPS, 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)

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)

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)

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)

Synthesizing 3D Abstractions by Inverting Procedural Buildings with Transformers. - Maximilian Dax; Jordi Berbel; Jan Stria; Leonidas J. Guibas; Urs Bergmann (CoRR, 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., 2025)

Reparameterized LLM Training via Orthogonal Equivalence Transformation. - Zeju Qiu; Simon Buchholz; Tim Z. Xiao; Maximilian Dax; Bernhard Schölkopf; Weiyang Liu (CoRR, 2025)