Search Results for author: Đorđe Miladinović

Found 6 papers, 2 papers with code

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

no code implementations26 Sep 2022 Đorđe Miladinović, Kumar Shridhar, Kushal Jain, Max B. Paulus, Joachim M. Buhmann, Mrinmaya Sachan, Carl Allen

In principle, applying variational autoencoders (VAEs) to sequential data offers a method for controlled sequence generation, manipulation, and structured representation learning.

Representation Learning

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

no code implementations22 Mar 2021 João B. S. Carvalho, João A. Santinha, Đorđe Miladinović, Joachim M. Buhmann

In clinical practice, regions of interest in medical imaging often need to be identified through a process of precise image segmentation.

Image Segmentation Inductive Bias +3

Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling

1 code implementation ICLR 2021 Đorđe Miladinović, Aleksandar Stanić, Stefan Bauer, Jürgen Schmidhuber, Joachim M. Buhmann

We show that augmenting the decoder of a hierarchical VAE by spatial dependency layers considerably improves density estimation over baseline convolutional architectures and the state-of-the-art among the models within the same class.

Density Estimation

Disentangled State Space Representations

no code implementations7 Jun 2019 Đorđe Miladinović, Muhammad Waleed Gondal, Bernhard Schölkopf, Joachim M. Buhmann, Stefan Bauer

Sequential data often originates from diverse domains across which statistical regularities and domain specifics exist.

regression Transfer Learning

Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness

no code implementations31 Oct 2018 Raphael Suter, Đorđe Miladinović, Bernhard Schölkopf, Stefan Bauer

The ability to learn disentangled representations that split underlying sources of variation in high dimensional, unstructured data is important for data efficient and robust use of neural networks.


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