| Robustly disentangled causal mechanisms: Validating deep representations for interventional robustness R Suter, D Miladinovic, B Schölkopf, S Bauer International Conference on Machine Learning, ICML 2019, 6056-6065, 2019 | 73 | 2019 |
| On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset MW Gondal, M Wüthrich, Đ Miladinović, F Locatello, M Breidt, V Volchkov, ... Advances in Neural Information Processing Systems, NeurIPS 2019, 15714-15725, 2019 | 63 | 2019 |
| Granger-causal attentive mixtures of experts: Learning important features with neural networks P Schwab, D Miladinovic, W Karlen Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 4846-4853, 2019 | 43 | 2019 |
| SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species Đ Miladinović, C Muheim, S Bauer, A Spinnler, D Noain, M Bandarabadi, ... PLoS computational biology 15 (4), e1006968, 2019 | 24 | 2019 |
| Disentangled state space representations Đ Miladinović, MW Gondal, B Schölkopf, JM Buhmann, S Bauer arXiv preprint arXiv:1906.03255, 2019 | 16 | 2019 |
| Interventional robustness of deep latent variable models R Suter, D Miladinovic, S Bauer, B Schölkopf arXiv, 1811.00007, 2018 | 15 | 2018 |
| Efficient and flexible inference for stochastic systems S Bauer, NS Gorbach, Ð Miladinović, JM Buhmann Advances in Neural Information Processing Systems, NeurIPS 2017, 6991-7001, 2017 | 7 | 2017 |
| Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling Đ Miladinović, A Stanić, S Bauer, J Schmidhuber, JM Buhmann International Conference on Learning Representations, ICLR 2021, 2021 | 6 | 2021 |
| Rapid and reversible control of human metabolism by individual sleep states N Nowak, T Gaisl, D Miladinovic, R Marcinkevics, M Osswald, S Bauer, ... Cell Reports 37 (4), 109903, 2021 | 3 | 2021 |
| On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset MW Gondal, M Wüthrich, Đ Miladinović, F Locatello, M Breidt, V Volchkov, ... Advances in Neural Information Processing Systems 32 20, 15661-15672, 2020 | 1 | 2020 |
| Disentangled state space models: Unsupervised learning of dynamics across heterogeneous environments Ð Miladinović, W Gondal, B Schölkopf, JM Buhmann, S Bauer DeepGen Workshop at ICLR 2019, 2019 | 1 | 2019 |
| Holistic Modeling in Medical Image Segmentation Using Spatial Recurrence JBS Carvalho, J Santinha, Đ Miladinović, C Cotrini, JM Buhmann | | 2021 |
| Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation J Carvalho, JA Santinha, Đ Miladinović, JM Buhmann arXiv preprint arXiv:2103.11713, 2021 | | 2021 |
| On Training Deep Generative Models with Latent Variables D Miladinovic ETH Zurich, 2021 | | 2021 |
| Natural age-related sleep-wake alterations onset prematurely in the Tg2576 mouse model of Alzheimer’s disease S Kollarik, CG Moreira, I Dias, D Bimbiryte, D Miladinovic, JM Buhmann, ... bioRxiv, 2021 | | 2021 |
| Supplementary Material for:” Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks” P Schwab, D Miladinovic, W Karlen | | |
Stefan BauerKTH Stockholm, CIFARVerified email at kth.se
