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Found 3 papers, 1 papers with code
Residual Quantization with Implicit Neural Codebooks
In this paper, we propose QINCo, a neural RQ variant which predicts specialized codebooks per vector using a neural network that is conditioned on the approximation of the vector from previous steps.
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.
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.
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