Djordje Miladinovic
Djordje Miladinovic
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Cited by
Cited by
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
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
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
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
Disentangled state space representations
Đ Miladinović, MW Gondal, B Schölkopf, JM Buhmann, S Bauer
arXiv preprint arXiv:1906.03255, 2019
Interventional robustness of deep latent variable models
R Suter, D Miladinovic, S Bauer, B Schölkopf
arXiv, 1811.00007, 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
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
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
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
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
Holistic Modeling in Medical Image Segmentation Using Spatial Recurrence
JBS Carvalho, J Santinha, Đ Miladinović, C Cotrini, JM Buhmann
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
On Training Deep Generative Models with Latent Variables
D Miladinovic
ETH Zurich, 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
Supplementary Material for:” Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks”
P Schwab, D Miladinovic, W Karlen
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