Training Mixture Models at Scale via Coresets

By Mario Lucic, Matthew Faulkner, Andreas Krause, Dan Feldman. 2017

30 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. Mario Lucic
2. Olivier Bachem
3. Andreas Krause
4. Nitin Kamra
5. Yan Liu
6. Umang Gupta
7. Zixuan Ke
8. Bing Liu

Citations in the corpus

• Deep Generative Dual Memory Network for Continual Learning (2017, background): “ples to discard as new tasks arrive (Lucic et al., 2017).”
• Gradient Episodic Memory for Continual Learning (2017, methodology): “Second, we did not investigate advanced memory management (such as building coresets of tasks [Lucic et al., 2017]).”
• Practical Coreset Constructions for Machine Learning (2017, methodology): “In fact, for a sequence of ( k, ε )-coresets C ε we have that Based on the framework presented in Chapter 2, the sensitivity can be bounded by O (cid:0) kλ − 4 and the pseudo-dimension of the associated function family is O (cid:0) (cid:1) Feldman et al., 2011, Lucic et al., 2017].”