8 citations · 10 across the 4 of their papers we have counts for
4 papers
A Data Cartography based MixUp for Pre-trained Language Models
Seo Yeon Park, Cornelia Caragea
MixUp is a data augmentation strategy where additional samples are generated during training by combining random pairs of training samples and their labels. However, selecting rand…
On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliency
Seo Yeon Park, Cornelia Caragea
A well-calibrated neural model produces confidence (probability outputs) closely approximated by the expected accuracy. While prior studies have shown that mixup training as a data…
Lipschitz-constrained Unsupervised Skill Discovery
Seohong Park, Jongwook Choi, Jaekyeom Kim +2
We study the problem of unsupervised skill discovery, whose goal is to learn a set of diverse and useful skills with no external reward. There have been a number of skill discovery…
Unsupervised Skill Discovery with Bottleneck Option Learning
Jaekyeom Kim, Seohong Park, Gunhee Kim
Having the ability to acquire inherent skills from environments without any external rewards or supervision like humans is an important problem. We propose a novel unsupervised ski…