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Parametric Adversarial Divergences are Good Task Losses for Generative Modeling., , , , , и . ICLR (Workshop), OpenReview.net, (2018)Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets.. ICLR (Workshop), (2015)The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training., , , , и . AISTATS, том 5 из JMLR Proceedings, стр. 153-160. JMLR.org, (2009)Deep Learning using Robust Interdependent Codes., , и . AISTATS, том 5 из JMLR Proceedings, стр. 312-319. JMLR.org, (2009)Why Does Unsupervised Pre-training Help Deep Learning?, , , и . AISTATS, том 9 из JMLR Proceedings, стр. 201-208. JMLR.org, (2010)A high-order feature synthesis and selection algorithm applied to insurance risk modelling., , , , и . Int. J. Bus. Intell. Data Min., 6 (3): 237-258 (2011)Quickly Generating Representative Samples from an RBM-Derived Process., , и . Neural Comput., 23 (8): 2058-2073 (2011)A Closer Look at the Optimization Landscapes of Generative Adversarial Networks, , , , и . (2019)cite arxiv:1906.04848.Clustering is Efficient for Approximate Maximum Inner Product Search., и . CoRR, (2015)Adding noise to the input of a model trained with a regularized objective, , , и . CoRR, (2011)