Training Deep Models Faster with Robust, Approximate Importance Sampling

By Tyler B. Johnson, Carlos Guestrin. 2018

154 citations recorded by the source corpus.

Subfields: Optimization & Training

How researchers see this paper

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

In Optimization & Training

1. Jaehong Yoon
2. Eunho Yang
3. Kangwook Lee
4. Yuji Roh
5. Steven Euijong Whang
6. Sung Ju Hwang
7. Changho Suh
8. Divyam Madaan

Citations in the corpus

• Online Coreset Selection for Rehearsal-based Continual Learning (2021, positive): “Importance sampling [13, 14, 38] strengthens the loss/gradients of important samples based on influence functions.”
• FairBatch: Batch Selection for Model Fairness (2020, background): “More recently, importance sampling techniques have been proposed for faster convergence (Loshchilov and Hutter, 2016; Alain et al., 2016; Stich et al., 2017; Csiba and Richt ´ arik, 2018; Katharopoulos and Fleuret, 2018; Johnson and Guestrin, 2018).”