Learning spectrograms with convolutional spectral kernels

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A4 Artikkeli konferenssijulkaisussa
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2020

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Department of Computer Science

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en

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10

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The 23rd International Conference on Artificial Intelligence and Statistics, pp. 3826-3836, Proceedings of Machine Learning Research ; Volume 108

Abstract

We introduce the convolutional spectral kernel (CSK), a novel family of non-stationary, nonparametric covariance kernels for Gaussian process (GP) models, derived from the convolution between two imaginary radial basis functions. We present a principled framework to interpret CSK, as well as other deep probabilistic models, using approximated Fourier transform, yielding a concise representation of input-frequency spectrogram. Observing through the lens of the spectrogram, we provide insight on the interpretability of deep models. We then infer the functional hyperparameters using scalable variational and MCMC methods. On small- and medium-sized spatiotemporal datasets, we demonstrate improved generalization of GP models when equipped with CSK, and their capability to extract non-stationary periodic patterns.

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Shen, Z, Heinonen, M & Kaski, S 2020, Learning spectrograms with convolutional spectral kernels. in S Chiappa & R Calandra (eds), The 23rd International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 108, JMLR, pp. 3826-3836, International Conference on Artificial Intelligence and Statistics, Palermo, Italy, 03/06/2020. < http://proceedings.mlr.press/v108/shen20a.html >

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https://urn.fi/URN:NBN:fi:aalto-202010025750

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[article-cris] Perustieteiden korkeakoulu / SCI