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We propose Gaussian processes for signals over graphs (GPG) using the apriori knowledge that the target vectors lie over a graph.
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2014
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2015
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G. Chowdhary, H. A. Kingravi, J. P. How, and P. A. Vela, “Bayesian nonparametric adaptive control using Gaussian processes,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 26, no. 3, pp. 537–550, March 2015
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R. L. de Queiroz and P. A. Chou, “Transform coding for point clouds using a Gaussian process model,” IEEE Trans. Image Process. , vol. 26, no. 7, pp. 3507–3517, July 2017
2017
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D. Moungsri, T. Koriyama, and T. Kobayashi, “Duration prediction using multiple Gaussian process experts for gpr-based speech synthesis,” IEEE Intl. Conf. Acoust. Speech Signal Process. , pp. 5495–5499, March 2017
2017
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H. Bertrand, M. Perrot, R. Ardon, and I. Bloch, “Classification of mri data using deep learning and Gaussian process-based model selection,” IEEE Intl. Symp. Biomedical Imaging (ISBI) , pp. 745–748, April 2017
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A. Venkitaraman, S. Chatterjee, and P. Händel, “Kernel Regression for Signals over Graphs,” ArXiv e-prints , Jun. 2017
2017
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O. Teke and P. P. Vaidyanathan, “Extending classical multirate signal processing theory to graphs-part i: Fundamentals,” IEEE Trans. Signal Process. , vol. 65, no. 2, pp. 409–422, Jan 2017
2017
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