@inproceedings{discovery10084670,
         journal = {ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning},
          volume = {27},
         address = {Bruges, Belgium},
       booktitle = {ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning},
          series = {European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
            year = {2019},
           title = {Conditional BRUNO: A neural process for exchangeable labelled data},
       publisher = {European Symposium on Artificial Neural Networks (ESANN)},
             url = {https://www.esann.org/node/19},
        abstract = {We present a neural process that models exchangeable sequences of high-dimensional complex observations conditionally on a set of labels or tags. Our model combines the expressiveness of deep neural networks with the data-efficiency 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 demonstrated on a challenging few-shot view reconstruction task which requires generalisation from short sequences of viewpoints.},
          author = {Korshunova, I and Gal, Y and Gretton, A and Dambre, J}
}