Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Nuria Oliver,Fernando Pérez-Cruz,Jose A. Lozano

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620539.jpg

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https://doi.org/10.1007/978-3-030-86520-7applied computing; communication systems; computer graphics; computer networks; computer security; comput

GLUT

Machine Learning and Knowledge Discovery in Databases. Research TrackEuropean Conference,

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Non-exhaustive Learning Using Gaussian Mixture Generative Adversarial Networksthe reason that real-life complex datasets may not follow a well-known data distribution. In this paper, we propose a new online non-exhaustive learning model, namely, Non-Exhaustive Gaussian Mixture Generative Adversarial Networks (NE-GM-GAN) to address these issues. Our proposed model synthesizes

Myosin

Generative Max-Mahalanobis Classifiers for Image Classification, Generation and?Moreicular, the Max-Mahalanobis Classifier (MMC)?[.], a special case of LDA, fits our goal very well. We show that our Generative MMC (GMMC) can be trained discriminatively, generatively or jointly for image classification and generation. Extensive experiments on multiple datasets show that GMMC achieve

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GEST

Principled Interpolation in Normalizing Flowsvely. Our experimental results show superior performance in terms of bits per dimension, Fréchet Inception Distance (FID), and Kernel Inception Distance (KID) scores for interpolation, while maintaining the generative performance.

Temporal-Lobe

Decoupling Sparsity and Smoothness in?Dirichlet Belief Networksn each layer, and smoothness is enforced on this subset. Extra efforts on modifying the models are also made to fix the issues which is caused by introducing these binary variables. Extensive experimental results on real-world data show significant performance improvements of ssDirBN over state-of-t

Hemiplegia

Learning Weakly Convex Sets in Metric Spacesensional algorithm. The second one is concerned with the Euclidean space equipped with the Manhattan distance. For this metric space, weakly convex sets form a union of pairwise disjoint axis-aligned hyperrectangles. We show that a weakly convex set that is consistent with a set of examples and cont

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