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Found 3 papers, 2 papers with code
Context-Aware Compilation of DNN Training Pipelines across Edge and Cloud
Experimental results show that our system not only adapts well to, but also draws on the varying contexts, delivering a practical and efficient solution to edge-cloud model training.
Ranked #2 on
Recommendation Systems
on MovieLens 1M
(Precision metric)
DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentation
To alleviate these problems, we propose a novel deep medical image segmentation framework called Dual Swin Transformer U-Net (DS-TransUNet), which might be the first attempt to concurrently incorporate the advantages of hierarchical Swin Transformer into both encoder and decoder of the standard U-shaped architecture to enhance the semantic segmentation quality of varying medical images.
Context-aware deep model compression for edge cloud computing
While deep neural networks (DNNs) have led to a paradigm shift, its exorbitant computational requirement has always been a roadblock in its deployment to the edge, such as wearable devices and smartphones.
Ranked #169 on
Image Classification
on CIFAR-10
