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Found 2 papers, 1 papers with code
Deep Learning Meets Projective Clustering
Based on this approach, we provide a novel architecture that replaces the original embedding layer by a set of $k$ small layers that operate in parallel and are then recombined with a single fully-connected layer.
Provable Filter Pruning for Efficient Neural Networks
We present a provable, sampling-based approach for generating compact Convolutional Neural Networks (CNNs) by identifying and removing redundant filters from an over-parameterized network.
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