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Found 7 papers, 4 papers with code
Tyche: Stochastic In-Context Learning for Medical Image Segmentation
Existing learning-based solutions to medical image segmentation have two important shortcomings.
Magnitude Invariant Parametrizations Improve Hypernetwork Learning
In this work, we identify a fundamental and previously unidentified problem that contributes to the challenge of training hypernetworks: a magnitude proportionality between the inputs and outputs of the hypernetwork.
UniverSeg: Universal Medical Image Segmentation
We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training.
Scale-Space Hypernetworks for Efficient Biomedical Imaging
We find that SSHN consistently provides a better accuracy-efficiency trade-off at a fraction of the training cost.
Trade-offs of Local SGD at Scale: An Empirical Study
As datasets and models become increasingly large, distributed training has become a necessary component to allow deep neural networks to train in reasonable amounts of time.
What is the State of Neural Network Pruning?
Neural network pruning---the task of reducing the size of a network by removing parameters---has been the subject of a great deal of work in recent years.
Image segmentation of liver stage malaria infection with spatial uncertainty sampling
Global eradication of malaria depends on the development of drugs effective against the silent, yet obligate liver stage of the disease.
Cultural Vocal Bursts Intensity Prediction
Image Segmentation
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