Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series

By Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch. 2018

16 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sjoerd van Steenkiste
2. Yoshua Bengio
3. J. Urgen Schmidhuber
4. Bernhard Schölkopf
5. Sergey Levine
6. Trevor Darrell
7. Dacheng Tao
8. Ben Poole

Citations in the corpus

• Weakly-Supervised Disentanglement Without Compromises (2020, background): “…(Karaletsos et al., 2015; Whitney et al., 2016; Fraccaro et al., 2017; Denton & Birodkar, 2017; Hsu et al., 2017; Yingzhen & Mandt, 2018; Locatello et al., 2018) and disentangling motion and pose from content (Hsieh et al., 2018; Fortuin et al., 2019; Deng et al., 2017; Goroshin et al., 2015).”
• Are Disentangled Representations Helpful for Abstract Visual Reasoning? (2019, background): “Disentangling pose, style, or motion from content are classical vision tasks that has been studied with different degrees of supervision [72, 79, 80, 34, 19, 14, 21, 36].”
• Recent Advances in Autoencoder-Based Representation Learning (2018, background): “Fortuin et al. [74] combine a variation of VQ-VAE with self-organizing maps to learn interpretable discrete representations of sequences.”