Overall framework of STiL. STiL encodes image-tabular data using $\phi$, decomposes modality-shared and -specific information through DCC $\psi$ (a), and outputs ...

Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...

Extracting and analyzing relevant medical information from large-scale databases such as biobanks poses considerable challenges. To exploit such "big data," attempts have focused on large sampling ...

To his credit, Kasy is a realist here. He doesn’t presume that any of these proposals will be easy to implement. Or that it will happen overnight, or even in the near future. The troubling question at ...

Posts from this topic will be added to your daily email digest and your homepage feed. If you want to tweak what’s on your feed, you can make a post and ask. If you want to tweak what’s on your feed, ...

Abstract: Unsupervised cross-domain reinforcement learning (RL) pretraining shows great potential for challenging continuous visual control but poses a big challenge. In this article, we propose cross ...

This article was co-authored with Emma Myer, a student at Washington and Lee University who studies Cognitive/Behavioral Science and Strategic Communication. In today’s digital age, social media has ...

Self-supervised reinforcement learning is a technique where agents learn useful representations and skills from the environment through self-generated tasks, such as predicting next states or learning ...

Abstract: To address the challenges of road segmentation in remote sensing images—such as elongated structures, blurred boundaries, and the high cost of manual annotations—we propose a semi-supervised ...