8 citations · 11 across the 2 of their papers we have counts for
3 papers
cs.LG2022★ 8 cited
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri +4
Deep learning is increasingly moving towards a transfer learning paradigm whereby large foundation models are fine-tuned on downstream tasks, starting from an initialization learne…
cs.MM2020★ 3 cited
LAMP: Label Augmented Multimodal Pretraining
Jia Guo, Chen Zhu, Yilun Zhao +4
Multi-modal representation learning by pretraining has become an increasing interest due to its easy-to-use and potential benefit for various Visual-and-Language~(V-L) tasks. Howev…
cs.LG2020
Reducing the Teacher-Student Gap via Spherical Knowledge Disitllation
Jia Guo, Minghao Chen, Yao Hu +3
Knowledge distillation aims at obtaining a compact and effective model by learning the mapping function from a much larger one. Due to the limited capacity of the student, the stud…