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

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書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track影響因子(影響力)




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track影響因子(影響力)學(xué)科排名




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書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track被引頻次




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track被引頻次學(xué)科排名




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書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track年度引用學(xué)科排名




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書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track讀者反饋學(xué)科排名

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Unsupervised Learning of Joint Embeddings for Node Representation and?Community Detectionve model called . for learning .oint .mbedding for .ode representation and .ommunity detection. . learns a community-aware node representation, i.e., learning of the node embeddings are constrained in such a way that connected nodes are not only “closer” to each other but also share similar communit

MAPLE

GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphssuch as subspace selection, ego-network, or community analysis. These models do not take into account the multifaceted interactions between the structure and attributes in the network. In this paper, we propose GraphAnoGAN, an anomalous snapshot ranking framework, which consists of two core componen

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容易生皺紋

Gaussian Process Encoders: VAEs with?Reliable Latent-Space UncertaintyHowever, the latent variance is not a reliable estimate of how uncertain the model is about a given input point. We address this issue by introducing a sparse Gaussian process encoder. The Gaussian process leads to more reliable uncertainty estimates in the latent space. We investigate the implicati

哭得清醒了

Variational Hyper-encoding Networks parameters are sampled from a distribution in the model space modeled by a hyper-level VAE. We propose a variational inference framework to implicitly encode the parameter distributions into a low dimensional Gaussian distribution. Given a target distribution, we predict the posterior distribution

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Principled Interpolation in Normalizing Flowsinear interpolations show unexpected side effects, as interpolation paths lie outside the area where samples are observed. This is caused by the standard choice of Gaussian base distributions and can be seen in the norms of the interpolated samples as they are outside the data manifold. This observa

清楚

CycleGAN Through the Lens of (Dynamical) Optimal Transporten elements of the domains. Following the seminal CycleGAN model, variants and extensions have been used successfully for a wide range of applications. However, although there have been some attempts, they remain poorly understood, and lack theoretical guarantees. In this work, we explore the implic

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