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Improved Bayesian Compression
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Compression of Neural Networks (NN) has become a highly studied topic in recent years. The main reason for this is the demand for industrial scale usage of NNs such as deploying them on mobile devices, storing them efficiently, transmitting them via band-limited channels and most importantly doing inference at scale. In this work, we propose to join the Soft-Weight Sharing and Variational Dropout approaches that show strong results to define a new state-of-the-art in terms of model compression.
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Group Pruning using a Bounded-Lp norm for Group Gating and Regularization
A bounded-L1 regularizer combined with exponential gating layers prunes neural network channels to exactly zero during training, compressing standard models by 30 to 75 percent with little accuracy loss.
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