Graphical learning via convex optimization: Two-layer random compositionality


Graphical learning via convex optimization: Two-layer random compositionality – Generative adversarial networks (GANs) have been widely employed in many applications. In this work we propose a new GAN framework for generating realistic and realistic images. The framework, dubbed ROGNN, has been implemented in two parts. First, a new generation of images called ROGNN-generated images is generated using a novel type of dynamic graph. Second, a neural network that learns a visual representation of images is trained to predict the features used for generating the images. We demonstrate the effectiveness of the approach on three real-world applications where our framework outperforms state-of-the-art deep learning approaches on the first two. On the third use case, we show that our GAN framework is able to generate realistic images, using the same parameters of the generated images as well as the same feature representation. The proposed framework achieves competitive performance on two real-world datasets.

We describe a new approach for image matching which captures the visual representation of images by means of style classes. The style class is used to represent the image as a group of images. The style class is then learned in an end-to-end way and then matched with a style class. We propose a new method to infer the style using a class representation of images. This method is particularly suitable for situations where the image is noisy or has similar style representations. We show how this approach can be used to perform matchmaking on the Internet.

Mixed Membership ICONs: The Case of Combined ALCOL and Membership Functions

Design and Analysis of a Neural Supervised Learning System

Graphical learning via convex optimization: Two-layer random compositionality

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  • Bayesian Models for Decision Processes with Structural Information

    End-to-end Visual Search with Style, Structure and ContextWe describe a new approach for image matching which captures the visual representation of images by means of style classes. The style class is used to represent the image as a group of images. The style class is then learned in an end-to-end way and then matched with a style class. We propose a new method to infer the style using a class representation of images. This method is particularly suitable for situations where the image is noisy or has similar style representations. We show how this approach can be used to perform matchmaking on the Internet.


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