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Parametric Adversarial Divergences are Good Task Losses for Generative Modeling., , , , , and . ICLR (Workshop), OpenReview.net, (2018)A Closer Look at the Optimization Landscapes of Generative Adversarial Networks, , , , and . (2019)cite arxiv:1906.04848.Clustering is Efficient for Approximate Maximum Inner Product Search., and . CoRR, (2015)Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations., , , , , , and . CoRR, (2023)A high-order feature synthesis and selection algorithm applied to insurance risk modelling., , , , and . Int. J. Bus. Intell. Data Min., 6 (3): 237-258 (2011)Manifold Parzen Windows., and . NIPS, page 825-832. MIT Press, (2002)Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets., , and . NIPS, page 1108-1116. (2015)Auto-Encoding Dictionary Definitions into Consistent Word Embeddings., and . EMNLP, page 1522-1532. Association for Computational Linguistics, (2018)Combining modality specific deep neural networks for emotion recognition in video., , , , , , , , , and 17 other author(s). ICMI, page 543-550. ACM, (2013)RATM: Recurrent Attentive Tracking Model., , , , and . CVPR Workshops, page 1613-1622. IEEE Computer Society, (2017)