1 code implementation • 18 Jan 2024 • Seong Jin Cho, Gwangsu Kim, Junghyun Lee, Jinwoo Shin, Chang D. Yoo
Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data.
no code implementations • 29 Sep 2021 • Seong Jin Cho, Gwangsu Kim, Chang D. Yoo
This strategy is valid only when the sample's "closeness" to the decision boundary can be estimated.
no code implementations • 1 Jan 2021 • Seong Jin Cho, Gwangsu Kim, Chang D. Yoo
Active learning strategy to query unlabeled samples nearer the estimated decision boundary at each step has been known to be effective when the distance from the sample data to the decision boundary can be explicitly evaluated; however, in numerous cases in machine learning, especially when it involves deep learning, conventional distance such as the $\ell_p$ from sample to decision boundary is not readily measurable.
no code implementations • ICLR 2019 • Seong Jin Cho, Sunghun Kang, Chang D. Yoo
Determining the appropriate batch size for mini-batch gradient descent is always time consuming as it often relies on grid search.