Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PFGE: Parsimonious Fast Geometric Ensembling of DNNs   Ensemble methods have been widely used to improve the performance of machine
learning methods in terms of generalization and uncertainty calibration, while
they struggle to use in deep learning systems, as training an ensemble of deep
neural networks (DNNs) and then deploying them for online prediction incur an
extremely higher computational overhead of model training and test-time
predictions. Recently, several advanced techniques, such as fast geometric
ensembling (FGE) and snapshot ensemble, have been proposed. These methods can
train the model ensembles in the same time as a single model, thus getting
around the hurdle of training time. However, their overhead of model recording
and test-time computations remains much higher than their single model based
counterparts. Here we propose a parsimonious FGE (PFGE) that employs a
lightweight ensemble of higher-performing DNNs generated by several
successively-performed procedures of stochastic weight averaging. Experimental
results across different advanced DNN architectures on different datasets,
namely CIFAR-$\{$10,100$\}$ and Imagenet, demonstrate its performance. Results
show that, compared with state-of-the-art methods, PFGE achieves better
generalization performance and satisfactory calibration capability, while the
overhead of model recording and test-time predictions is significantly reduced.
Our code is available at https://github.com/ZJLAB-AMMI/PFGE.

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The categories of this paper are: dg-ga
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
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The categories of this paper are: alg-geom
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The categories of this paper are: supr-con
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-th