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Conditional BRUNO: A Neural Process for Exchangeable Labelled Data
2020
Neurocomputing
We present a neural process which models exchangeable sequences of highdimensional complex observations conditionally on a set of labels or tags. Our model combines the expressiveness of deep neural networks with the dataefficiency of Gaussian processes, resulting in a probabilistic model for which the posterior distribution is easy to evaluate and sample from, and the computational complexity scales linearly with the number of observations. The advantages of the proposed architecture are
doi:10.1016/j.neucom.2019.11.108
fatcat:xxfcss33lfh45bzna4y72mltyy