To some, METR’s “time horizon plot” indicates that AI utopia—or apocalypse—is close at hand. The truth is more complicated.
The information presented here is intended to describe the course goals for current and prospective students as well as others who are interested in our courses. It is not intended to replace the ...
This is where AI-augmented data quality engineering emerges. It shifts data quality from deterministic, Boolean checks to ...
This is a PyTorch implementation of the GraphATA algorithm, which tries to address the multi-source domain adaptation problem without accessing the labelled source graph. Unlike previous multi-source ...
Abstract: Cross-domain few-shot medical image segmentation (CD-FSMIS) offers a promising and data-efficient solution for medical applications where annotations are severely scarce and multimodal ...
Multimodal Graph-Based Domain Adaptation With Color and Texture Prompts for Plant Image Segmentation
Abstract: This paper proposes a novel domain adaptation network to improve model accuracy in plant disease detection by leveraging structured image and text graphs. The proposed methodology constructs ...
Background: Graph Convolutional Networks (GCNs) are widely applied for spatial domain identification in spatial transcriptomics (ST), where node representations are learned by aggregating information ...
This is a PyTorch implementation of the GraphRTA algorithm, which tries to address the open-set graph domain adaptation problem, where the goal is to not only correctly classify target nodes into the ...
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