Mixed-Membership Recursive Partitioning


Mixed-Membership Recursive Partitioning – Generative adversarial networks (GANs) have been successfully used to generate a large amount of informative data by leveraging prior learning results to optimize each latent variable. In this work, we propose a new hierarchical neural network model for supervised classification that leverages a combination of the prior- and latent-structured information in order to learn the latent weights. The new model, which can be considered as a recurrent neural network, is trained directly from the raw information in the structured data, which has a very strong correlation between the labels. We further further show that the supervised classification performance of the new model is highly dependent on its prior knowledge and we derive a learning criterion where the prior knowledge from the structured data is used to predict the label. We also show that we can accurately predict the label for a variable using our discriminative network representation of the structured data. Empirical evaluation is available demonstrating the superior performance of our new model over its state-of-the-art counterparts.

In this work, we propose a new technique for multi-view learning (MSL) that integrates the use of image and image pair representations with semantic feature learning. Specifically, we propose a new recurrent neural network architecture for multiple views and a recurrent neural network architecture for multiple views with semantic feature features. We show that our multi-view multi-view learning method achieves better performance than existing MSL methods.

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Mixed-Membership Recursive Partitioning

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  • Robust Multi-View 3D Pose Estimation via Ground Truth Information

    TernWise Regret for Multi-view Learning with Generative Adversarial NetworksIn this work, we propose a new technique for multi-view learning (MSL) that integrates the use of image and image pair representations with semantic feature learning. Specifically, we propose a new recurrent neural network architecture for multiple views and a recurrent neural network architecture for multiple views with semantic feature features. We show that our multi-view multi-view learning method achieves better performance than existing MSL methods.


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