| 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 | 183 | 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 | 151 | 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, 2019 | 71 | 2019 |
| Granger-causal attentive mixtures of experts: Learning important features with neural networks P Schwab, D Miladinovic, W Karlen AAAI Conference on Artificial Intelligence, 2019 | 61 | 2019 |
| 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, 2021 | 41 | 2021 |
| Disentangled state space representations Đ Miladinović, MW Gondal, B Schölkopf, JM Buhmann, S Bauer Advances in Neural Information Processing Systems (NeurIPS), 2019 | 26 | 2019 |
| Interventional robustness of deep latent variable models R Suter, D Miladinovic, S Bauer, B Schölkopf arXiv, 1811.00007, 2018 | 19 | 2018 |
| 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 | 10 | 2021 |
| Efficient and flexible inference for stochastic systems S Bauer, NS Gorbach, Ð Miladinović, JM Buhmann Advances in Neural Information Processing Systems (NeurIPS), 2017 | 9 | 2017 |
| Natural age-related slow-wave sleep alterations onset prematurely in the Tg2576 mouse model of Alzheimer’s disease S Kollarik, I Dias, CG Moreira, D Bimbiryte, D Miladinovic, JM Buhmann, ... Neurodegenerative Diseases, 2022 | 4 | 2022 |
| Disentangled state space models: Unsupervised learning of dynamics across heterogeneous environments Ð Miladinović, W Gondal, B Schölkopf, JM Buhmann, S Bauer DeepGen Workshop (ICLR), 2019 | 4 | 2019 |
| Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs Đ Miladinović, K Shridhar, K Jain, MB Paulus, JM Buhmann, C Allen Advances in Neural Information Processing Systems (NeurIPS), 2022 | 2 | 2022 |
| Holistic Modeling in Medical Image Segmentation Using Spatial Recurrence JBS Carvalho, J Santinha, Đ Miladinović, C Cotrini, JM Buhmann Medical Imaging with Deep Learning (MIDL), 2021 | 1 | 2021 |
| On Training Deep Generative Models with Latent Variables D Miladinovic ETH Zurich, 2021 | 1 | 2021 |
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