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Dynamic Probabilistic Pruning: Training sparse networks based on stochastic and dynamic masking
Deep Learning (DL) models are known to be heavily over-parametrized, resulting in a large memory footprint and power consumption.
Active Deep Probabilistic Subsampling
Subsampling a signal of interest can reduce costly data transfer, battery drain, radiation exposure and acquisition time in a wide range of problems.
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