no code implementations • 31 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.
no code implementations • 7 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.
no code implementations • 22 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.
no code implementations • 26 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.
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.
4 code implementations • NeurIPS 2019 • Muhammad Waleed Gondal, Manuel Wüthrich, Đorđe Miladinović, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer
Learning meaningful and compact representations with disentangled semantic aspects is considered to be of key importance in representation learning.