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Found 8 papers, 4 papers with code
Adversarial Predictions of Data Distributions Across Federated Internet-of-Things Devices
Despite this, recent works have demonstrated that data reconstruction can be done with the locally trained model updates which are communicated across the network.
EXSCLAIM! -- An automated pipeline for the construction of labeled materials imaging datasets from literature
Due to recent improvements in image resolution and acquisition speed, materials microscopy is experiencing an explosion of published imaging data.
A Two-stage Framework for Compound Figure Separation
We propose a two-stage framework to address the proposed compound figure separation problem.
Scaling Distributed Training of Flood-Filling Networks on HPC Infrastructure for Brain Mapping
Mapping all the neurons in the brain requires automatic reconstruction of entire cells from volume electron microscopy data.
Training on the Edge: The why and the how
Edge computing is the natural progression from Cloud computing, where, instead of collecting all data and processing it centrally, like in a cloud computing environment, we distribute the computing power and try to do as much processing as possible, close to the source of the data.
Distributed, Parallel, and Cluster Computing
U-SLADS: Unsupervised Learning Approach for Dynamic Dendrite Sampling
Novel data acquisition schemes have been an emerging need for scanning microscopy based imaging techniques to reduce the time in data acquisition and to minimize probing radiation in sample exposure.
SLADS-Net: Supervised Learning Approach for Dynamic Sampling using Deep Neural Networks
From these results we observe that deep neural network based training results in superior performance when the training and testing images are not similar.
Reduced Electron Exposure for Energy-Dispersive Spectroscopy using Dynamic Sampling
Analytical electron microscopy and spectroscopy of biological specimens, polymers, and other beam sensitive materials has been a challenging area due to irradiation damage.
