Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity

By Tianyi Zhou, J. Bilmes. 2018

63 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. Tyler L. Hayes
2. Christopher Kanan
3. Giri Panamoottil Krishnan
4. Xiaoming Liu
5. Maxim Bazhenov
6. Ying Tai
7. Wenwu Zhu
8. Kushal Kafle

Citations in the corpus

• Replay in Deep Learning: Current Approaches and Missing Biological Elements (2021, positive): “…has been shown to speed up the training of neural networks, while also leading to better generalization (Bengio et al., 2009; Graves, Bellemare, Menick, Munos, & Kavukcuoglu, 2017; Hunziker et al., 2019; Zhou & Bilmes, 2018; Fan, Tian, Qin, Li, & Liu, 2018; Achille, Rovere, & Soatto, 2018).”
• A Survey on Curriculum Learning (2021, background): “Other works [118], [144] also design “harder examples first” curricula.”
• CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition (2020, background): “Learning from easier samples first and harder samples later is a common strategy in Curriculum Learning (CL) [2, 42].”