A statistical perspective on distillation
By Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Seungyeon Kim, Sanjiv Kumar. 2021
107 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.
- Lorenzo Bonicelli
- Matteo Boschini
- Angelo Porrello
- Simone Calderara
- Pietro Buzzega
- Laura Schmid
- Namgyu Ho
- Se-Young Yun
Citations in the corpus
- Class-Incremental Continual Learning Into the eXtended DER-Verse (2022, positive): “We here build upon the seminal work of [24], which has recently proposed a statistical background of Knowledge Distillation (KD) helping researchers and practitioners to gain novel insights on its effectiveness.”
- Large Language Models Are Reasoning Teachers (2022, methodology): “We note that this scaling effect is in contrast not a given in knowledge distillation, where more accurate teachers do not always result in better students (Menon et al., 2021).”