no code implementations • 29 Sep 2021 • Minsub Lee, Junhyun Park, Sojin Jang, Chanhui Lee, Hyungjoo Cho, Minsuk Shin, Sungbin Lim
Recently, Bootstrapping (Attentive) Neural Processes (B(A)NP) propose a bootstrap method to capture the functional uncertainty which can replace the latent variable in (Attentive) Neural Processes ((A)NP), thus overcoming the limitations of Gaussian assumption on the latent variable.
2 code implementations • NeurIPS 2021 • Minsuk Shin, Hyungjoo Cho, Hyun-seok Min, Sungbin Lim
Bootstrapping has been a primary tool for ensemble and uncertainty quantification in machine learning and statistics.
no code implementations • 13 Jun 2019 • Sungwoong Kim, Ildoo Kim, Sungbin Lim, Woonhyuk Baek, Chiheon Kim, Hyungjoo Cho, Boogeon Yoon, Taesup Kim
In this paper, a neural architecture search (NAS) framework is proposed for 3D medical image segmentation, to automatically optimize a neural architecture from a large design space.
no code implementations • 6 Jun 2018 • YoungJu Jo, Hyungjoo Cho, Sang Yun Lee, Gunho Choi, Geon Kim, Hyun-seok Min, YongKeun Park
Recent advances in quantitative phase imaging (QPI) and artificial intelligence (AI) have opened up the possibility of an exciting frontier.
1 code implementation • 23 Oct 2017 • Hyungjoo Cho, Sungbin Lim, Gunho Choi, Hyun-seok Min
Consequently our model does not only transfers initial stain-styles to the desired one but also prevent the degradation of tumor classifier on transferred images.