Ðorðe Miladinovic

According to our database, Ðorðe Miladinovic authored at least 13 papers between 2017 and 2022.

Collaborative distances:

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

2022

Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs.

Djordje Miladinovic, Kumar Shridhar, Kushal Jain, Max B. Paulus, Joachim M. Buhmann, Carl Allen

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Holistic Modeling In Medical Image Segmentation Using Spatial Recurrence.

João B. S. Carvalho, João A. Santinha, Djordje Miladinovic, Carlos Cotrini, Joachim M. Buhmann

Proceedings of the International Conference on Medical Imaging with Deep Learning, 2022

2021

On Training Deep Generative Models with Latent Variables.

Djordje Miladinovic

PhD thesis, 2021

Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation.

João B. S. Carvalho, João A. Santinha, Djordje Miladinovic, Joachim M. Buhmann

CoRR, 2021

Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling.

Ðorðe Miladinovic, Aleksandar Stanic, Stefan Bauer, Jürgen Schmidhuber, Joachim M. Buhmann

Proceedings of the 9th International Conference on Learning Representations, 2021

2019

SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species.

Ðorðe Miladinovic, Christine Muheim, Stefan Bauer, Andrea Spinnler, Daniela Noain, Mojtaba Bandarabadi, Benjamin Gallusser, Gabriel Krummenacher, Christian R. Baumann, Antoine Adamantidis, Steven A. Brown, Joachim M. Buhmann

PLoS Comput. Biol., 2019

Disentangled State Space Representations.

Ðorðe Miladinovic, Muhammad Waleed Gondal, Bernhard Schölkopf, Joachim M. Buhmann, Stefan Bauer

CoRR, 2019

On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset.

Muhammad Waleed Gondal, Manuel Wuthrich, Djordje Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

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

Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness.

Raphael Suter, Ðorðe Miladinovic, Bernhard Schölkopf, Stefan Bauer

Proceedings of the 36th International Conference on Machine Learning, 2019

Disentangled State Space Models: Unsupervised Learning of dynamics across Heterogeneous Environments.

Ðorðe Miladinovic, Muhammad Waleed Gondal, Bernhard Schölkopf, Joachim M. Buhmann, Stefan Bauer

Proceedings of the Deep Generative Models for Highly Structured Data, 2019

Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks.

Patrick Schwab, Djordje Miladinovic, Walter Karlen

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Interventional Robustness of Deep Latent Variable Models.

Raphael Suter, Ðorðe Miladinovic, Stefan Bauer, Bernhard Schölkopf

CoRR, 2018

2017

Efficient and Flexible Inference for Stochastic Systems.

Stefan Bauer, Nico S. Gorbach, Ðorðe Miladinovic, Joachim M. Buhmann

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017