13,911 research outputs found
Neural networks in geophysical applications
Neural networks are increasingly popular in geophysics.
Because they are universal approximators, these
tools can approximate any continuous function with an
arbitrary precision. Hence, they may yield important
contributions to finding solutions to a variety of geophysical applications.
However, knowledge of many methods and techniques
recently developed to increase the performance
and to facilitate the use of neural networks does not seem
to be widespread in the geophysical community. Therefore,
the power of these tools has not yet been explored to
their full extent. In this paper, techniques are described
for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size
and architecture
Bayesian Compression for Deep Learning
Compression and computational efficiency in deep learning have become a
problem of great significance. In this work, we argue that the most principled
and effective way to attack this problem is by adopting a Bayesian point of
view, where through sparsity inducing priors we prune large parts of the
network. We introduce two novelties in this paper: 1) we use hierarchical
priors to prune nodes instead of individual weights, and 2) we use the
posterior uncertainties to determine the optimal fixed point precision to
encode the weights. Both factors significantly contribute to achieving the
state of the art in terms of compression rates, while still staying competitive
with methods designed to optimize for speed or energy efficiency.Comment: Published as a conference paper at NIPS 201
A Layer Decomposition-Recomposition Framework for Neuron Pruning towards Accurate Lightweight Networks
Neuron pruning is an efficient method to compress the network into a slimmer
one for reducing the computational cost and storage overhead. Most of
state-of-the-art results are obtained in a layer-by-layer optimization mode. It
discards the unimportant input neurons and uses the survived ones to
reconstruct the output neurons approaching to the original ones in a
layer-by-layer manner. However, an unnoticed problem arises that the
information loss is accumulated as layer increases since the survived neurons
still do not encode the entire information as before. A better alternative is
to propagate the entire useful information to reconstruct the pruned layer
instead of directly discarding the less important neurons. To this end, we
propose a novel Layer Decomposition-Recomposition Framework (LDRF) for neuron
pruning, by which each layer's output information is recovered in an embedding
space and then propagated to reconstruct the following pruned layers with
useful information preserved. We mainly conduct our experiments on ILSVRC-12
benchmark with VGG-16 and ResNet-50. What should be emphasized is that our
results before end-to-end fine-tuning are significantly superior owing to the
information-preserving property of our proposed framework.With end-to-end
fine-tuning, we achieve state-of-the-art results of 5.13x and 3x speed-up with
only 0.5% and 0.65% top-5 accuracy drop respectively, which outperform the
existing neuron pruning methods.Comment: accepted by AAAI19 as ora
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