Faster Attend-Infer-Repeat with Tractable Probabilistic Models

By Karl Stelzner, Robert Peharz, Kristian Kersting. 2019

61 citations recorded by the source corpus.

Subfields: Generative Models

How researchers see this paper

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

In Generative Models

1. Dit-Yan Yeung
2. Hao Wang
3. J. Urgen Schmidhuber
4. Klaus Greff
5. Dirk Weissenborn
6. Jakob Uszkoreit
7. Sara Sabour
8. Aleksandar Stani'c

Citations in the corpus

• Object-Centric Learning with Slot Attention (2020, background): “[41] Karl Stelzner, Robert Peharz, and Kristian Kersting.”
• Conditional Object-Centric Learning from Video (2021, background): “…2019; Engelcke et al., 2020; Crawford & Pineau, 2019; Lin et al., 2020; Locatello et al., 2020; Du et al., 2021a) or videos (van Steenkiste et al., 2018; Kosiorek et al., 2018; Stelzner et al., 2019; Kipf et al., 2020; Crawford & Pineau, 2020; Creswell et al., 2021) without explicit supervision.”
• A Survey on Bayesian Deep Learning (2016, methodology): “Following AIR, variants such as Fast AIR [105] and Sequential AIR [60] are proposed to improve its computational efficiency and performance.”
• R-SQAIR: Relational Sequential Attend, Infer, Repeat (2019, background): “SPAIR [6] scales AIR to scenarios with many objects and SuPAIR[28] improves speed and robustness of learning in AIR.”