Search Results for author:
Found 4 papers, 1 papers with code
Dependable Distributed Training of Compressed Machine Learning Models
The existing work on the distributed training of machine learning (ML) models has consistently overlooked the distribution of the achieved learning quality, focusing instead on its average value.
Matching DNN Compression and Cooperative Training with Resources and Data Availability
To make machine learning (ML) sustainable and apt to run on the diverse devices where relevant data is, it is essential to compress ML models as needed, while still meeting the required learning quality and time performance.
Efficient Distributed DNNs in the Mobile-edge-cloud Continuum
In the mobile-edge-cloud continuum, a plethora of heterogeneous data sources and computation-capable nodes are available.
Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation
First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image.
