Adaptive Sampling for SGD by Exploiting Side Information

By Siddharth Gopal. 2016

67 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. Yoshua Bengio
2. Percy Liang
3. Eric Learned-Miller
4. Carlos Guestrin
5. Dacheng Tao
6. Andrew McCallum
7. Wenwu Zhu
8. Trevor Darrell

Citations in the corpus

• Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples (2017, methodology): “Selecting more difficult instances can accelerate learning or improve testing accuracy [18, 29, 13].”
• Training Deep Models Faster with Robust, Approximate Importance Sampling (2018, methodology): “[3], [4], and [6] propose algorithms based on similar point estimation strategies.”
• A Survey on Curriculum Learning (2021, background): “Other works [118], [144] [69], [101] or the gradient magnitude [1], [28].”