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Training Sparse Neural Networks
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Deep neural networks with lots of parameters are typically used for large-scale computer vision tasks such as image classification. This is a result of using dense matrix multiplications and convolutions. However, sparse computations are known to be much more efficient. In this work, we train and build neural networks which implicitly use sparse computations. We introduce additional gate variables to perform parameter selection and show that this is equivalent to using a spike-and-slab prior. We experimentally validate our method on both small and large networks and achieve state-of-the-art compression results for sparse neural network models.
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Cited by 1 Pith paper
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Principled Approximation Methods for Efficient and Scalable Deep Learning
A thesis that synthesizes the author's published work on continuous approximations to discrete deep learning problems, with experiments showing efficiency gains, but offering little new beyond the author's prior papers.
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